The Norwegian River Monitoring Programme 2019 – Water Quality Status and Trends Photo: NIVA Photo

REPORT SNO. 7564-2020

The Norwegian river monitoring programme 2019 – water quality status and trends Photo: NIVA Photo:

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Title Serial number Date The Norwegian river monitoring programme 2019 – water quality status 7564-2020 15th December 2020 and trends Elveovervåkningsprogrammet 2019 – vannkvalitetsstatus og -trender

Author(s) Topic group Distribution Hans Fredrik Veiteberg Braaten, Cathrine Brecke Gundersen, Øyvind Environmental Open Kaste, James Sample, Dag Øystein Hjermann, Magnus Dahler Norling, monitoring Jose-Luis Guerrero Calidonio, Ian Allan, Luca Nizzetto Geographical area Pages Norway 87 + appendices

Client(s) Client's reference Norwegian Environment Agency M-1817 | 2020

Client's publication: Printed NIVA Project number 16384

Summary In the Norwegian River Monitoring Programme (in Norwegian: Elveovervåkingsprogrammet) 20 rivers along the Norwegian coastline are monitored for chemical and hydrological parameters. It is a continuation of river monitoring with data from 1990. This report presents the current status (2019) and long-term (1990-2019) water quality trends.

Four keywords Fire emneord 1. Rivers 1. Elver 2. Monitoring 2. Overvåking 3. Water quality 3. Vannkvalitet 4. Trends 4. Trender

This report is quality assured in accordance with NIVA's quality system and approved by: Hans Fredrik Veiteberg Braaten François Clayer Sondre Meland Project Manager Quality Assurance Research Manager ISBN 978-82-577-7299-4 NIVA-report ISSN 1894-7948 © Norsk institutt for vannforskning/Norwegian Institute for Water Research & Norwegian Environment Agency. The publication can be cited freely if the source is stated.

The Norwegian river monitoring programme 2019 – water quality status and trends

NIVA 7564-2020

Preface

The Norwegian river monitoring programme is a main component of the Norwegian water authorities’ surveillance monitoring in rivers, according to the requirements set by the EU Water Framework Directive (WFD). The monitoring also fulfils Norway’s obligations under the Oslo-Paris Convention (OSPAR). Since 2017, the Norwegian Institute for Water Research (NIVA), in collaboration with consortium partners, has carried out the monitoring activities. Results from the 2019 monitoring activities are presented in four thematic reports, where this report includes results from the basic monitoring of 20 rivers across Norway. The 20 rivers are selected to represent the variability in river water quality and fluxes, and to cover a substantial fraction of the riverine flux from mainland Norway to the sea.

In 2019, the work presented in this report was a collaboration between NIVA, the Norwegian Water Resources and Energy Directorate (NVE), and Eurofins Environment Testing Norway AS.

Hans Fredrik Veiteberg Braaten (NIVA) was project leader for the river monitoring programme in 2019, together with Cathrine Brecke Gundersen. Other co-workers at NIVA responsible for the results in the present report include Øyvind Kaste (catchment modelling, evaluation of sensor data), James Sample (databases, calculation of riverine loads, TEOTIL modelling), José-Luis Calidonio (catchment and TEOTIL modelling), Luca Nizzetto (catchment modelling), Ian Allan (catchment modelling), Rolf Høgberget (sensor monitoring), Dag Ø. Hjermann (climate and hydrology data), Liv Bente Skancke (coordination of local field work personnel, quality assurance of sampling and chemical analyses), Jan Karud (development of maps), and Elisabeth Lie and Marit Villø (contact persons at the NIVA chemical laboratory). Quality assurance of the report has been carried out by François Clayer.

At NVE, Trine Fjeldstad has been responsible for the local sampling programmes, Stein Beldring has carried out the hydrological modelling, and Morten N. Due has been the administrative contact. In 2019, Eurofins carried out the total nitrogen and parts of the mercury analyses.

Contact persons at The Norwegian Environment Agency have been Gunn Lise Haugestøl, Preben Danielsen and Eivind Farmen. Thanks to all involved for a good collaboration.

Oslo, 30.11.2020

Hans Fredrik Veiteberg Braaten

NIVA 7564-2020

Table of contents

1. Introduction ...... 12 1.1 Monitoring objectives ...... 12 1.2 Main rivers ...... 13 1.3 Additional Rivers ...... 15

2. Methods ...... 17 2.1 Water discharge ...... 17 2.2 Water temperature ...... 17 2.2 Water quality sampling and analyses ...... 18 2.2.1 Sampling methodology ...... 18 2.2.2 Chemical parameters – detection limits and analytical methods ...... 19 2.2.3 Quality assurance and direct on-line access to data ...... 20 2.2.4 Sampling and analyses for additional rivers ...... 21 2.3 Calculation of riverine loads...... 21 2.4 Trend analyses and data comparison ...... 22 2.4.1 Trend analysis methodology ...... 22 2.4.2 Selection of rivers ...... 22 2.4.3 Selection of parameters and time-periods ...... 23 2.5 Comparison of metals data with EU WFD quality standards ...... 24 2.6 Modelling of contaminants in the Alna river ...... 24 2.7 Sensor monitoring in Rivers Storelva and Målselva...... 27

3. Results and Discussions ...... 28 3.1 Climate and hydrology: status and trends ...... 28 3.1.1 Air temperature and precipitation in 2019 ...... 28 3.1.2 Trends in air temperature and precipitation 1980-2019 ...... 29 3.1.3 Water temperature – status 2019 and trends ...... 30 3.1.4 Water discharge – status 2019 and trends ...... 31 3.2 Water quality status 2019 ...... 33 3.2.1 pH and calcium ...... 33 3.2.2 Suspended matter (turbidity, SPM, and silica) ...... 35 3.2.3 Organic carbon ...... 37 3.2.4 Nutrients ...... 39 3.2.5 Metals ...... 43 3.3 Additional Rivers - Water quality status 2019 ...... 51 3.3.1 pH and calcium ...... 51 3.3.2 Suspended matter, (turbidity, SPM, and silica) ...... 51 3.3.3 Organic carbon ...... 52 3.3.4 Nutrients ...... 53 3.3.5 Metals ...... 55 3.4 Trends in riverine loads and concentrations ...... 56 3.4.1 Loads and concentrations of SPM, silica, TOC, and nutrients (1990-2019) ...... 56 3.4.2 Loads and concentrations of metals (2004-2019) ...... 59 3.4.3 A comparison of methods for Hg flux calculations ...... 61

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3.5 Quality of dissolved organic matter ...... 62 3.5.1 Main Rivers ...... 62 3.5.2 Additional Rivers ...... 65 3.6 Modelling of contaminants in the Alna river ...... 67 3.6.1 Calibration results ...... 67 3.6.2 Discussion of climate drivers affecting contaminant transport ...... 73 3.7 High-frequency monitoring in Rivers Storelva and Målselva...... 74 3.7.1 River Storelva ...... 74 3.7.2 River Målselva ...... 78

4. Conclusion ...... 81

5. References ...... 82

6. Appendix A ...... 88 6.1 Riverine concentrations in 2019 ...... 88 6.2 Riverine loads in 2019 ...... 108

NIVA 7564-2020

Summary

The Norwegian River Monitoring Programme features monitoring for various chemical, physical, and hydrological parameters in 20 rivers distributed along the Norwegian coastline. The monitoring programme is a main component of the Norwegian water authorities’ surveillance of rivers, according to the requirements set by the EU Water Framework Directive (WFD), and it also forms the basis for the fulfilment of Norway’s obligations under the Oslo-Paris Convention (OSPAR). This report presents 2019 water quality status and the up to 30-year trends (1990-2019) for the 20 rivers. Additionally, data from the National monitoring program for limed rivers, including 6 rivers in the south and south-western parts of Norway, were included.

Overall, 2019 was the 20th warmest year since the measurements started in 1900. Air temperatures were 1.2 oC above the 1961-1990 normal. The geographical differences were large, with southern parts of the country having a warmer year than normal and the northern parts a colder or more normal year. For 17 out of 20 monitoring stations, there is a significant increasing temperature trend since the early 1980s. There was 15 % more precipitation than normal in 2019, making it the 15th wettest year recorded the last 120 years.

The river catchments display large variation in elevation, vegetation and soils types, affecting the water chemistry. Generally, most variables follow a geographical pattern east-to-west and south-to- north, typically illustrated by pH and calcium (Ca) concentrations. In 2019, river pH ranged from weakly acidic (pH 6.2) to basic (pH 7.9). The most acidic rivers are found in the south and south-west due to historical acid deposition and slow weathering bedrock. Consequently, several of these rivers were low in Ca, and according to the Norwegian WFD typology fall into the categories very low (< 1 mg/L) and low (1-4 mg/L). In the south-east, where the catchments typically consist of boreal forests, pH was weakly acidic to neutral and in mid-to-northern Norway, the pH was generally close to neutral. Organic carbon, measured as total organic carbon (TOC), also fall into these categories based on catchment typography. Bjerkreimselva, Vikedalselva, Vosso, Vefsna, Driva, and Målselva have very clear water (TOC < 2mg/L) according to the Norwegian WFD typology, while Glomma, Alna, Drammenselva, Numedalslågen, Skienselva, Otra, Nausta, Orkla, Nidelva, Altaelva, Tana and Pasvikelva are clear (2 mg/L < TOC < 5 mg/L), and Storelva and Orreelva are humic (TOC > 5 mg/L). There are two clear exceptions from these patterns, Alna and Orreelva, both heavily influenced by human activities through urbanization and agriculture, respectively. These rivers typically have higher pH values and Orreelva also elevated TOC concentrations, likely due to effluent inputs and diffuse water discharge from agriculture.

Turbidity and suspended particulate matter (SPM) – typically influencing aqueous light penetration and transport of metals and/or nutrients – were much higher in Orreelva than in the other rivers, consistent with influence from agriculture and particles from erodible clays. Glomma and Alna are also rivers with typically high measurements of suspended matter, but both turbidity and SPM concentrations were lower in 2019 compared to the previous five years.

Concentrations of total phosphorous (tot-P) and total nitrogen (tot-N) are much higher in Alna and Orreelva compared to the other rivers in the monitoring programme, again due to urban and agricultural activities in the respective catchments. Both rivers have less than good ecological status according to the good/moderate boundary for both parameters for their respective water. Of the remaining rivers, only Glomma and Numedalslågen had elevated tot-P concentrations, but both were below the good/moderate boundary. Elevated levels of tot-N were also evident for some of the rivers in the south and south-western part of Norway, likely an effect of atmospheric deposition.

NIVA 7564-2020

Total metals concentrations were generally high in the urban river Alna, including arsenic (As), lead (Pb), cadmium (Cd), copper (Cu), zinc (Zn), chromium (Cr), nickel (Ni), and mercury (Hg). But mean annual 2019 concentrations were lower than the five-year mean for all metals where historical data exists. Pasvikelva also shows elevated concentrations of selected metals, most noteworthy Ni and Cu, likely due to metallurgical activity on the Russian side of the border. Orkla (Cd, Cu, Zn, Ni), Storelva (As, Pb, Cd) and Orreelva (As, Pb, Ni) had moderately elevated concentrations of some metals, likely from runoff from old main tailings in the catchments (Orkla and Storelva) and from agricultural activities (Orreelva).

The statistical analysis of trends of loads of the different parameters for a sub-selection of rivers revealed a geographical pattern: there was a significant increase in the loads and concentrations of silica (SiO2), PO4, and tot-N for the south-eastern rivers Glomma, Drammenselva, and Numedalslågen. Additionally, significant trends in the loads, but not in the concentrations, were evident in Drammenselva for SPM, TOC, and tot-P, in Numedalslågen for tot-P and NH4, and in Orreelva for SPM, SiO2, and tot-P. In contrast, decreasing trends in loads and concentrations were evident in Vefsna for SPM, tot-P, PO4, tot-N, NH4, and NO3. Parallel trends in loads and concentrations reflect a change in the sources of the considered parameters to the river in addition to possible changes in discharge. For metal loads, the general pattern is significantly declining trends in loads and concentrations. The only two exceptions are Vefsna and Alta, where Ni concentrations are significantly increasing. Although the reason for this is not known, the Ni concentrations are low and does not warrant major concern at this point. For the first time, Hg loads were calculated, revealing highest flux of Hg in 2019 in Glomma (46.4 kg). The Hg load from Glomma was nearly four times as high as in the other rivers: 13.5 kg in Pasvikelva, 12.7 kg in Tanaelva, 10.7 kg in Drammenselva, and 9.5 kg in Skienselva.

Generally, the long-term monitoring of river water in Norway (1990-2019) does not display a significant increase in TOC concentrations (Drammenselva is the only exception) and no river show significant trends in the export of TOC. Browning of surface waters across the northern hemisphere is a well-established phenomenon, consisting of an observed increase in both the concentration and colour of DOM, explained by a reduction of acid deposition and/or to climate change. The lack of expected TOC increase in the large Norwegian rivers included here are likely explained by 1) too short time series to capture any potential increase, and 2) regulation of rivers for hydropower. Point 2) is particularly important as studies have shown that water flow is the major factor governing interannual variation in organic matter (OM) concentration in rivers.

For Norway as a whole, the mean seasonal TOC concentration in 2019 was increasing from winter through summer, dropping in august before a continued increase during autumn. This reflects the typical seasonal events of increased production and transport of OM to the river during spring warming with snow melt. The drop in TOC concentration in august was likely caused by a combination of high biological uptake and low precipitation that reduced transport of new OM. During autumn, intensive rainfall ensured increased transport of TOC from the forest floors to the rivers. The rivers draining into the North Sea generally show the lowest TOC levels, high water discharge and relatively high DOM aromaticity and molecular size. The Skagerrak region rivers had the highest TOC levels, moderate water discharge, but also high DOM aromaticity and molecular size. The Barents Sea rivers generally had intermediate TOC levels, low water discharge and the lowest aromaticity and molecular size. These regional variations should be explored further in combination with data on land use, climate, and other catchment characteristics.

An integrated catchment model for contaminants (INCA-Tox) was applied to simulate fate and transport of two PCB congeners (PCB-101, PCB-153) and three PAH congeners (Phenanthrene, Fluoranthene, Benzo-a-Pyrene) in Alna. Hydrology (2006-2015), suspended sediments, carbon, and

NIVA 7564-2020 contaminants were calibrated based on measured data. The selected contaminants are largely hydrophobic with a large affinity to OM, implying that concentrations of contaminants in soil OM will determine the dissolved concentration in soil water, which again is the main driver for the river dissolved concentrations. By choosing initial concentrations in soil organic carbon (SOC) within the ranges given by data from forested areas around Oslo, we obtained river dissolved contaminant concentrations that matched the general magnitude of data measured during the period 2013-2016. Boreal forested catchments are currently in a transitory phase, where the system will shift from acting as a net sink of atmospheric PCBs to becoming a net source for air and water environments.

Short-term effects of climate variability on water chemistry were studied using high-frequency (hourly) sensor data from Storelva and Målselva, including water temperature, pH, conductivity, turbidity and fluorescent DOM (fDOM). Data from Storelva show that flood characteristics (i.e. type, magnitude, timing) largely influenced short-time variation in concentrations of dissolved ions (conductivity), suspended particles (turbidity) and DOM in 2019. Also in Målselva, turbidity values showed a strong response to repeated flood peaks during a high-flow period in late spring. Values were especially high (<300 NTU) during a major flood peak that occurred in early July. fDOM on the other hand were highest in the early phase of the snowmelt period and decreased during the major flood peak in early July.

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Sammendrag

Tittel: Elveovervåkningsprogrammet 2019 – vannkvalitetsstatus og -trender År: 2020 Forfatter(e): Hans Fredrik Veiteberg Braaten, Cathrine Brecke Gundersen, Øyvind Kaste, James Sample, Dag Øystein Hjermann, Magnus Dahler Norling, Jose-Luis Guerrero Calidonio, Ian Allan, Luca Nizzetto Utgiver: Norsk institutt for vannforskning, ISBN 978-82-577-7299-4

Elveovervåkingsprogrammet omfatter månedlig overvåking av ulike kjemiske, fysiske og hydrologiske parametere i 20 elver fordelt geografisk langs norskekysten. Programmet er en viktig del av norske myndigheters basisovervåking av elver i henhold til vannforskriften, i tillegg til at programmet oppfyller Norges forpliktelser i henhold til Oslo-Paris konvensjonen (OSPAR). Denne rapporten presenterer status for 2019 og langtidstrender (opptil 30 år, 1990-2019) av vannkvalitet for de 20 elvene. I tillegg er data fra seks elver fra Tiltaksovervåking av kalkede laksevassdrag i Norge inkludert.

2019 var det 20. varmeste året siden målingene startet i 1900. Den gjennomsnittlige lufttemperaturen i Norge var 1,2o C over normalen for perioden 1961-1990. Den geografiske variasjonen var stor, der de sørlige delene av landet hadde et varmere år enn normalt, mens nordlige områder hadde kaldere eller mer normale temperaturer. For 17 av de 20 overvåkingsstasjonene i programmet viser data signifikant økende temperaturer siden tidlig 1980- tall. Det var også 15 % mer nedbør enn normalt i Norge i 2019, noe som tilsvarer det 15. våteste året de siste 120 årene.

De overvåkede nedbørfeltene har ulike karakteristika, som for eksempel stor variasjon i høyde over havet, vegetasjon og jordtyper, og dette påvirker vannkjemien i elvene. Generelt følger de fleste målte parametere et geografisk mønster (øst-til-vest og sør-til-nord), illustrert ved for eksempel pH og kalsiumkonsentrasjoner. I 2019 varierte pH i elvene fra svakt surt (pH 6,2) til basisk (pH 7,9). De sureste elvene var i sør- og sørvestlige deler av landet. Dette skyldes en kombinasjon av naturlig lav bufferkapasitet og det faktum at disse områdene er – og har vært – utsatt for sur nedbør. Som et resultat av dette har disse elvene lave kalsiumkonsentrasjoner og typifiseres som svært kalkfattig (< 1 mg Ca/L) og kalkfattig (1-4 mg Ca/L), i henhold til Vannforskriften. I sørøstlige, boreale områder, var elvene svakt sure eller nøytrale, mens elvene lenger nord (midt- til Nord-Norge) var typisk nøytrale. Et lignende geografisk mønster observeres for organisk karbon i vann, målt som totalt organisk karbon (TOC). Bjerkreimselva, Vikedalselva, Vosso, Vefsna, Driva og Målselva kan typifiseres som veldig klare (TOC < 2 mg/L), Glomma, Alna, Drammenselva, Numedalslågen, Skienselva, Otra, Nausta, Orkla, Nidelva, Altaelva, Tana og Pasvikelva er klare (2 mg/L < TOC < 5 mg/L), mens Storelva og Orreelva er humøse (TOC > 5 mg/L). I denne sammenhengen skiller Alna og Orreelva seg tydelig ut, begge tungt påvirket av menneskelig aktivitet gjennom henholdsvis urbanisering/industri og landbruk. Alna og Orreelva har høyere pH-verdier og Orreelva også forhøyede TOC-konsentrasjoner, sannsynligvis pga. tilførsel av avløpsvann og utslipp fra jordbruk.

Turbiditet og suspendert partikulært materiale (SPM) – parametere som indikerer hvordan lysforhold og transport av metaller og næringsstoffer påvirkes i elvene – hadde mye høyere nivåer i Orreelva sammenlignet med de andre elvene. Dette er typisk der vannet påvirkes av landbruk og jorderosjon. Glomma og Alna har også tradisjonelt høye målinger av suspendert materiale, men både turbiditet og SPM var lavere i 2019 sammenlignet med gjennomsnittet for de fem foregående årene (2014-2018).

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Næringsstoffene fosfor (P) og nitrogen (N) forekommer i mye høyere konsentrasjoner i Alna og Orreelva sammenlignet med de andre elvene i programmet. Dette er også en effekt av menneskelige aktiviteter og landbruksvirksomhet i respektive nedbørfelt. Begge elvene overskred Vannforskriftens grense for god tilstand for sine respektive vanntyper, basert på totale konsentrasjoner (tot-N og tot- P). Av de andre elvene var det bare Glomma og Numedalslågen som hadde forhøyede tot-P konsentrasjoner, men begge innenfor god tilstand. Forhøyede nivåer av tot-N ble dokumentert for enkelte elver i sør og sørøstlige deler av Norge, sannsynligvis en effekt av atmosfærisk deposisjon.

Som et resultat av nærliggende industriaktivitet hadde Alna høye konsentrasjoner av arsen, bly, kadmium, kobber, sink, krom, nikkel og kvikksølv, men nivåene i 2019 var lavere enn gjennomsnittet i perioden 2014-2018 for alle metaller der historiske data finnes. I Pasvikelva ble det observert høye konsentrasjoner av nikkel og kadmium, sannsynligvis pga. betydelig metallurgisk industriaktivitet i grensenære områder i Russland. Orkla (kadmium, kobber, sink, nikkel), Storelva (arsen, bly, kadmium) og Orreelva (arsen, bly, nikkel) hadde moderat forhøyede konsentrasjoner av enkelte metaller, der sannsynlige kilder inkluderer avrenning fra historisk gruvedrift i nedbørfeltet (Orkla og Storelva) og landbruksaktivitet (Orreelva).

Den statistiske analysen av tilførselstrender for et utvalg nedbørfelt i programmet avslørte et geografisk mønster: det var en signifikant økning i tilførsler og konsentrasjoner av silisiumdioksid (SiO2), fosfat (PO4) og tot-N for de sørøstlige elvene Glomma, Drammenselva og Numedalslågen. I tillegg var det signifikante trender i tilførsler, men ikke i konsentrasjoner, i Drammenselva av SPM, TOC og tot-P, i Numedalslågen av tot-P og ammonium (NH4), og i Orreelva av SPM, SiO2 og tot-P. Dette i contrast til Vefsna, der trendene i både tilførsler og konsentrasjoner av SPM, tot-P, PO4, tot- N, NH4 og NO3 var signifikant nedadgående. Parallelle trender i tilførsler og konsentrasjoner reflekterer en endring i kilden for den aktuelle parameteren, i tillegg til en mulig endring i vanntilførsel. For metalltilførsler er det generelle mønsteret at trendene er nedadgående. Det er to unntak: Vefsna og Alta, der nikkelkonsentrasjonene øker signifikant. Årsaken til dette er ikke kjent, men konsentrasjonene er lave og det er ikke grunnlag for bekymring foreløpig. For første gang ble også kvikksølvtilførsler beregnet for elvene i programmet. Tilførslene var størst i Glomma (46,4 kg), nesten fire ganger mer enn i noen annen elv: 13,5 kg i Pasvikelva, 12,7 kg i Tanaelva, 10,7 kg i Drammenselva, og 9,5 kg i Skienselva.

Generelt er det ikke mulig å observere noen signifikant økning i TOC-konsentrasjoner (Drammenselva eneste unntaket) eller TOC-tilførsler i langtidsovervåkingen av elvevann i Norge (1990-2019). Brunere overflatevann i innsjøer og elver er en veletablert realitet over store deler av den nordlige halvkule, en effekt av redusert sur nedbør og økt nedbør som fører til økt utlekking av organisk materiale (OM) fra jordsmonnet i nedbørfeltene. Den manglende trenden i store norske elver skyldes sannsynligvis 1) for korte tidsserier, og 2) regulering av vassdragene. Reguleringen er særlig viktig ettersom studier har vist at vannføring er den viktigste faktoren for å forklare år-til-år variasjon av OM-konsentrasjoner.

For Norge samlet var det i 2019 en økning i gjennomsnittlig månedlig TOC-konsentrasjon fra vinteren og gjennom sommeren, med en nedgang i nivået i august, før konsentrasjonene økte igjen utover høsten. Dette reflekterer et typisk sesongmønster med økt produksjon og transport av OM til elvene ved varmere temperaturer og snøsmelting om våren. Lavere konsentrasjoner i august skyldes sannsynligvis høyt biologisk opptak og lite nedbør (som gir redusert transport). Gjennom høsten fører intense nedbørsperioder til økt transport av OM fra skogbunn til elv. Elvene som drenerer til Nordsjøen hadde lavest TOC-konsentrasjoner, høy vannføring og relativt høy DOM-aromatisitet og molekylstørrelse. Elvene i Skagerrak-regionen hadde høyest TOC-konsentrasjoner, moderat vannføring, men også høy DOM-aromatisitet og molekylstørrelse. I Barentsregionen hadde elvene generelt et middels TOC-innhold, lav vannføring og den laveste DOM-aromatisitet og

NIVA 7564-2020 molekylstørrelse. Disse regionale variasjonene i OM-kvalitet bør undersøkes nærmere i kombinasjon med data for arealutnyttelse, klima og andre nedbørfeltkarakteristika.

En integrert nedbørfeltmodell for miljøgifter (INCA-Tox) ble brukt for å simulere mobilisering og transport av to PCB-forbindelser (PCB-101, PCB-153) og tre PAH-forbindelser (fenantren, fluoranten, benzo-a-pyren) i Alna. Hydrologi (2006-2015), suspenderte sedimenter, organisk karbon og miljøgifter ble kalibrert i forhold til målte data. De valgte PCB- og PAH-forbindelsene er i stor grad hydrofobe med stor affinitet til organisk materiale. Dette innebærer at konsentrasjoner av miljøgifter i jorda vil styre hvor mye som kan løses i jordvannet og deretter i elvevannet. Ved å benytte jorddata fra skogkledde områder rundt Oslo var det mulig å simulere nivåer av løste miljøgiftforbindelser som samsvarte med konsentrasjonene som er målt i elva i perioden 2013-2016. Boreale nedbørfelt er i en overgangsfase der systemet om kort tid vil endre seg fra å være et sluk for atmosfærisk PCB til å bli en netto kilde for PBC til luft og vann.

Korttidseffekter av klimavariasjon på vannkjemi ble studert ved bruk av høyoppløselige (timesverdier) sensordata fra Storelva og Målselva, inkludert vanntemperatur, pH, konduktivitet, turbiditet og løst organisk materiale (fDOM). Data fra Storelva viste at flommer har stor innvirkning på vannkvaliteten i elva, men responsen på de ulike vannkvalitetsparameterne varierer med flomtype, flomstørrelse og tidspunkt på året flommene inntreffer. Også i Målselva viste dataene at turbiditeten responderte sterkt på gjentatte flomtopper som oppsto i løpet av snøsmeltingsperioden. Spesielt høy turbiditet ble målt under flomtoppen i begynnelsen av juli. fDOM var høyest tidlig i snøsmeltingsperioden, mens målingene i motsetning til turbiditeten avtok under den største flomtoppen i juli.

NIVA 7564-2020

1. Introduction

The river monitoring programme (Elveovervåkingsprogrammet) was established in 2017, replacing the former RID programme (Riverine inputs and direct discharges to Norwegian coastal waters) that had been running since 1990. The programme includes monitoring of 20 rivers (Table 1 and Figure 1) for various chemical, physical, and hydrological parameters. The main features of the programme are: 1) relatively high sampling frequency (monthly at all sites and for all parameters, except for metals); 2) an extended list of chemical variables (including emerging contaminants and priority substances); 3) the use of catchment models for simulation of climate effects and contaminant discharges on water quality; and 4) sensor monitoring in selected rivers (determining water temperature, pH, conductivity, turbidity and fluorescent dissolved organic matter (FDOM)). The 20 monitored rivers were all part of the RID programme, but the monitoring frequency has changed: minimum monthly since 1990 for 11 of the rivers (with two exceptions where monitoring started later); quarterly since 1990 for 8 of the rivers; and annually from 1990 to 2003 for 1 of the rivers (Braaten et al., 2017). For more information on the differences between the current and the past programme, see the report for the 2017 river monitoring results (Kaste et al., 2018).

1.1 Monitoring objectives

The Norwegian river monitoring programme is the basis for fulfilment of Norway’s obligations under the Oslo-Paris Convention (OSPAR) and is also a main component of the Norwegian water authorities’ surveillance monitoring in rivers, according to the requirements set by the EU Water Framework Directive (WFD). The main objectives of the river monitoring programme, formulated by the Norwegian Environment Agency, are to: 1. document status and long-term trends for nutrient and contaminant concentrations in Norwegian rivers 2. obtain data for classification of Norwegian rivers according to the requirements of the WFD 3. reveal water quality changes that can be attributed to climate change or other human influences 4. increase the knowledge base on climate processes affecting water 5. increase current knowledge related to the fates of emerging contaminants in aquatic ecosystems 6. provide data that may explain changes in eutrophication and contaminant levels along the Norwegian coast 7. estimate riverine inputs and direct discharges of nutrients and contaminants to Norwegian coastal waters (for reporting under the OSPAR Convention)

Data collected as part of the river monitoring programme in 2019 are presented in four separate reports. The present report addresses objectives 1, 3, 4, and partly 6 and 7 by providing the current status (2019) and long-term water quality trends (1990-2019) for 20 rivers selected to represent most of the Norwegian drainage area. The other reports include: i) “Classification of ecological and chemical status of Norwegian rivers according to the requirements of the WFD – The river monitoring programme 2019” (In Norwegian), addressing objective 2; ii) “Source apportioned input of nutrients to Norwegian coastal areas in 2019 – tables, charts and maps” (In Norwegian), addressing partly objective 7, and iii) «Priority substances and emerging contaminants in selected

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Norwegian rivers», addressing objectives 1, 5, 6 and 7. Additionally, the monitoring programme provides an information repository for the newly developed TEOTIL metals model, addressing partly objectives 6 and 7.

1.2 Main rivers The 20 rivers sampled within this monitoring programme discharge to (from south to north) Skagerrak, the North Sea, the Norwegian Sea and the Barents Sea (Table 1). The rivers are selected based on geographical location (Table 1, Figure 1), availability of historical data, relevance in relation to land-use (Figure 2) and pollution pressure, and access to existing infrastructure for sampling.

Table 1. Rivers included in the programme. River name UTM UTM UTM Catchment Waterbody Drainage (east) (north) zone (km2) code basin ID Glomma* 621600 6573156 32 41918 002-1519-R Skagerrak Alna* 600213 6642144 32 69 006-71-R Skagerrak Drammenselva* 556636 6624287 32 17034 012-2399-R Skagerrak Numedalslågen* 561346 6551822 32 5577 015-33-R Skagerrak Skienselva* 534726 6562938 32 10772 016-769-R Skagerrak Storelva** 498897 6503307 32 408 018-127-R Skagerrak Otra* 438737 6449755 32 3738 021-28-R Skagerrak Bjerkreimselva 325246 6487028 32 705 027-92-R North Sea Orreelva* 299152 6515475 32 105 028-16-R North Sea Vikedalselva 325319 6599745 32 118 038-11-R North Sea Vosso* 336048 6727293 32 1492 062-219-R North Sea Nausta 327402 6826450 32 277 084-218-R North Sea 109-54-R Norwegian Driva 477383 6948637 32 2487 Sea 121-56-R Norwegian Orkla* 237185 7018935 33 3053 Sea 123-29-R Norwegian Nidelva 569352 7030201 32 3110 Sea 151-36-R Norwegian Vefsna* 418710 7292351 33 4122 Sea Målselva 406570 7660047 34 3239 196-275-R Barents Sea Altaelva* 586586 7759686 34 7373 212-63-R Barents Sea Tana 543964 7791926 35 16389 234-124-R Barents Sea Pasvikelva 386937 7709634 36 18404 246-65242-L Barents Sea * “Main rivers” in the previous RID programme, monthly monitoring since 1990 (except Rivers Vosso and Alna, monthly from 2008 and 2013, respectively) ** Also denoted “Vegårdselva” in the RID database Of the rivers included, Glomma, Drammenselva, Numedalslågen, Skienselva, Otra, Bjerkreimselva, Driva, Orkla, Nidelva, Vefsna, Målselva, Altaelva, and Pasvikelva are regulated.

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Figure 1: Map showing the location of the rivers in the Norwegian river monitoring programme, including drainage areas (purple) and the sampling sites (red dot).

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Figure 2: Land use for the 20 rivers included in the monitoring programme. Shown are % land-use, including developed (red), agriculture (yellow), forest (green), other vegetation (grey), swamp/peat (blue), snow/glacier (white), freshwater (light blue), sea (very light blue), not mapped (dark grey), and areas outside Norway without data available (black).

1.3 Additional Rivers

This year’s report also covers the water chemistry for six additional rivers (Table 2 and Figure 3). These rivers were part of the National monitoring program for limed rivers (In Norwegian: Tiltaksovervåking av kalkede laksevassdrag i Norge). The National monitoring program for limed rivers covers rivers in the south and south-western Norway that are limed to counter the effects from acid deposition. Although acid deposition has decreased since the 1970s, the critical load for acid deposition (especially in the form of nitrogen) is still exceeded for these catchments.

Table 2. Additional rivers included in the report

River name UTM UTM UTM Catchment Waterbody (east) (north) zone (km2) ID Nidelva 478798 6474111 32 4025 019-398-R Tovdalselva 449503 6456437 32 1885 020-183-R Mandalselva 413351 6453264 32 1809 022-654-R Lygna 390778 6454254 32 663 024-412-R Suldalslågen 344680 6596924 32 1463 036-92-R Ekso 325747 6737576 32 414 063-181-R

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Figure 3: Map showing the location of the additional rivers included with water chemistry in this report. Drainage areas are illustrated with purple shading and the sampling sites from the liming programme status in red.

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2. Methods

2.1 Water discharge For 11 of the rivers (the “main rivers” of the previous RID programme plus Alna, Table 1) discharge data was downloaded from Norwegian Water Resources and Energy Directorate (NVE, Hydra-II database). Since the hydrological stations are usually not located exactly at the same site as where the water samples were collected, the water discharge has been calculated by up- or downscaling, proportional to the respective drainage area. For the remaining 9 rivers, water discharge has been simulated with a spatially distributed version of the HBV-model (Beldring et al., 2003). The use of this model was introduced in 2004, and Skarbøvik et al. (2017) gives more information on the methodology.

2.2 Water temperature Data on water temperature is acquired from four different sources (Table 3): Sensor monitoring (hourly time-step, see section 2.7), TinyTag temperature loggers (hourly time-step), manual measurements with a thermometer in connection with the monthly water quality sampling, and NVE temperature logging (daily averages from bi-hourly measurements). For the former three the measurements were made at the water quality sampling sites, while the NVE loggers were at stations located in close vicinity to these sites. The TinyTag loggers were secured to land and deployed in the river at the water quality sampling site. They were routinely replaced each autumn to ensure enough battery capacity.

Since temperature measurements have only been part of the river monitoring programme since 2013, data from NVE has been used for long-term trend analysis. This includes data for rivers were other data sources are used for the current monitoring (to get data from the actual sampling sites). Details on the time series from the closest NVE station in each river are presented in Table 4. Long- term data series of water temperature typically contain some missing data. Prior to trend analysis, the data was filtered to remove years for which >90% of the daily observations were missing.

Table 3. Sources for water temperature data in monitored rivers

Data source Sites Sensor-based Storelva TinyTag loggers Skienselva, Otra, Numedalslågen, Altaelva, Vefsna, Orreelva* and Vosso NVE station Orkla and Vikedalselva Manual measurement Drammenselva, Driva, Glomma, Alna, Bjerkreimselva, Nausta, Nidelva, Målselva, Tana, Pasvikelva * The TinyTag logger in Orreelva was not found during field work in June 2020, and data from July to December 2019 were ignored. For more details we refer to result section 3.1.3.

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Table 4. Stations with available long-term data on water temperature. The stations are operated by the Norwegian Water Resources and Energy Directorate (NVE).

St. ID River name Water temperature station Start End 29617 Glomma 2.1078.0.1003.1 Glomma ovf. 2007 2019 Sarpefossen 36225 Alna n.a. 29612 Drammenselva 12.298.0.1003.4 Drammenselva 1987 2019 v/Døvikfoss 29615 Numedalslågen 15.115.0.1003.1 Numedalslågen 2005 2018* v/Brufoss 29613 Skienselva 16.207.0.1003.2 Skienselva ndf. Norsjø 1991 2019 30019 Storelva n.a. 29614 Otra 21.79.0.1003.1 Otra v/Mosby 1988 2018* 29832 Bjerkreimselva 27.29.0.1003.1 Bjerkreimselvi 1987 2018* v/Bjerkreim 29783 Orreelva n.a. 29837 Vikedalselva 38.2.0.1003.1 Vikedalselva utløp 1987 2019 29821 Vosso 62.30.0.1003.3 Vosso ovf. Evangervatnet 1993 2017* 29842 Nausta 84.23.0.1003.3 Nausta v/Hovefossen 1999 2017* 29822 Driva 109.44.0.1003.2 Driva ndf. Grøa 29778 Orkla 121.62.0 Orkla v/Merk Bru 1992 2019 29844 Nidelva n.a. 29782 Vefsna 151.32.0.1003.3 Vefsna v/Laksfors 2014 2015* 29848 Målselv 196.35.0.1003.1 Malangfoss 29779 Altaelva 212.68.0.1003.1 Alta v/Gargia 1982 2018* 29820 Tanaelva 234.19.0.1003.1 Tana ovf. Polmakelva 2000 2018* 29819 Pasvikelva 246.11.0.1003.1 Pasvikelva v/Skogfoss 1992 2018* kraftstasjon *Updated temperature was not available for 2019 in time for this report.

2.2 Water quality sampling and analyses

2.2.1 Sampling methodology Monthly sampling was conducted by grab sampling, undertaken by local fieldworkers (Skarbøvik et al., 2017). In Rivers Glomma and Drammenselva, both receiving a substantial part of their water discharge from high-elevation areas, additional sampling was conducted during May and June to get a better representation of the high-flow period following snowmelt.

In 2019, the monitoring station at Målselva was moved approximately 17 km downstream (Table 1). Prior to 2017, two stations were monitored in this region: Målselva and Barduelva, the latter being a major tributary. Barduelva was removed from the monitoring programme in 2017 and the station at Målselva has now been shifted to a location downstream of the confluence, in order to integrate discharges from both river systems.

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2.2.2 Chemical parameters – detection limits and analytical methods The parameters monitored in 2019, including information on methodology and limits of detection (LOD) and quantification (LOQ) are given in Table 5. The metals, including silver (Ag), arsenic (As), cadmium (Cd), chromium (Cr), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) were analysed every three months (i.e. four times per year).

Mercury (Hg) was analysed for the first time this year with two different methods. The samples collected every three months were analysed by atomic absorption spectrometry (AAS) with a method limit of quantification (LOQ) 1.0 ng/L (hereafter: the AAS method). Additionally, separate samples were collected and analysed every month using USEPA method 1631, oxidation, purge and trap, and cold vapor atomic fluorescence spectrometry (CVAFS) with a LOQ 0.2 ng/L (hereafter: the CVAFS method) (Table 5).

Table 5. Analytical methods, limits of detection (LOD) and quantification (LOQ)

Parameter LOD/LOQ Analytical Method pH n.a. NS-EN ISO 10523 Conductivity (mS/m) 0.03/0.1 NS-ISO 7888 Turbidity (FNU) 0.1/0.3 NS-EN ISO 7027 Suspended particulate matter (SPM) 0.1 mg/l when NS 4733 modified (mg/L) 1 L is filtered Total Organic Carbon (TOC) and Dissolved NS 1484 modified 0.03/0.1 Organic Carbon (DOC) (mg C/L) Total phosphorus (tot-P) and total NS 4725 – Peroxodisulphate oxidation 0.3/1 dissolved phosphorus (TDP) (µg P/L) method modified (automated) Orthophosphate (PO4-P) (µg P/L) NS 4724 – Automated molybdate method 0.3/1 modified (automated) Total nitrogen (tot-N) (µg N/L) NS 4743 – Peroxodisulphate oxidation 3.3/10 method

Nitrate (NO3-N) (µg N/L) 0.7/2 NS-EN ISO 10304-1

Ammonium (NH4-N) (µg N/L) 0.7/2 NS-EN ISO 14911 Calcium (mg/L) NS-EN ISO 17294-1 and NS EN ISO 17294-2 0.0017/0.005 modified Particulate Organic Carbon (POC) and Dep. on blank NS-EN ISO/IEC 17025, Test 009 particulate Nitrogen (PN) & vol. filtered UV-visible absorbance spectrum Internal method n.a. (900 nm – 200 nm) Silicone (Si) (Si/ICP; mg Si/L) 0.008/0.025 NS-EN ISO 16264 modified Silver (Ag) (µg Ag/L) NS-EN ISO 17294-1 and NS EN ISO 17294-2 0.0007/0.0020 modified Arsenic (As) (µg As/L) NS-EN ISO 17294-1 and NS EN ISO 17294-2 0.008/0.025 modified Cadmium (Cd) (µg Cd/L) NS-EN ISO 17294-1 and NS EN ISO 17294-2 0.0010/0.0030 modified Chromium (Cr) (µg Cr/L) NS-EN ISO 17294-1 and NS EN ISO 17294-2 0.008/0.025 modified Copper (Cu) (µg Cu/L) NS-EN ISO 17294-1 and NS EN ISO 17294-2 0.013/0.040 modified Mercury (Hg) (ng Hg/L) 0.3/1.0 NS-EN ISO 12846 modified (AAS method)

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0.1/0.2 USEPA 1631 (CVAFS method) Nickel (Ni) (µg Ni/L) NS-EN ISO 17294-1 and NS EN ISO 17294-2 0.013/0.040 modified Lead (Pb) (µg Pb/L) NS-EN ISO 17294-1 and NS EN ISO 17294-2 0.0017/0.005 modified Zinc (Zn) (µg Zn/L) NS-EN ISO 17294-1 and NS EN ISO 17294-2 0.05/0.15 modified

2.2.3 Quality assurance and direct on-line access to data Data from the chemical analyses were transferred to the NIVA database and quality checked against historical data by researchers with long experience in assessing water quality data. If any anomalies were found, the samples were re-analysed or data removed from the final dataset. The data are available on-line at www.aquamonitor.no/RID, where users can view values and graphs for each of the monitored rivers. In Table 6, information on the total number of samples analysed and the fraction of measurements below the LOQ for the various parameters are summarised.

Table 6. Proportion of analyses below limits of quantification (LOQ) in 2019

Number of Number below % below LOQ Parameter samples LOQ Conductivity 248 0 0 pH 259 0 0 Ca 248 0 0

SiO2 248 0 0 SPM 248 32 13 TOC 248 0 0 TOT-P 248 1 0.4

PO4-P 248 50 20 TOT-N 248 0 0

NO3-N 248 6 2.4

NH4-N 248 87 35 As 80 1 1.3 Pb 80 0 0 Cd 80 19 24 Cu 80 0 0 Zn 80 3 3.8 Cr 80 0 0 Ni 80 1 1.3 Hg (AAS method) 80 58 73 Hg (CVAFS method) 238 0 0 Ag 80 56 70

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2.2.4 Sampling and analyses for additional rivers The additional rivers from the National monitoring program for limed rivers programme were sampled in the same way as the regular rivers of the programme: monthly grab sampling. The samples were analysed at Vestfold laboratory. In Table 7, the number of samples analysed and the fraction of the samples being below the LOQ for the various parameters included in the report are summarised for the additional rivers. In addition to the parameters listed in Table 7, these samples were analysed for UV-Vis absorbance (200 – 800 nm by 0.5 nm) to describe the quality of the dissolved organic matter (DOM) in the samples (filtered through 0.45 µm pore size).

Table 7. Proportion of analyses below limits of quantification (LOQ) for the additional rivers in 2019

Number of Number below % below LOQ Parameter samples LOQ pH 71 0 0.0 Ca 71 0 0.0 SPM 71 0 0.0 Turbidity 71 0 0.0

SiO2 71 0 0.0 TOC 71 0 0.0 DOC 71 0 0.0 POC 71 14 20 TOTN 71 0 0.0 NO3.N 71 0 0.0 NH4.N 71 21 30 PO4-P 71 49 69 As 71 0 0.0 Pb 71 34 48 Cd 71 44 62 Cu 71 5 7.0 Zn 71 0 0 Cr 71 24 34 Ni 71 49 69 Hg 71 71 100 Ag 71 70 98

2.3 Calculation of riverine loads

Estimates of annual riverine loads were done according to the formula below, which follows recommendations in OSPAR Agreement 2014:04; §6.13b. The method handles irregular sampling frequency and allows flood samples to be included in the annual load calculations.

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n Qi •Ci •ti Load = Q 1 r n Qi •ti 1 where:

Qi represents the water discharge at the day of sampling (day i);

Ci the concentration at day i;

ti the time period from the midpoint between day i-1 and day i to the midpoint between day i and day i+1, i.e., half the number of days between the previous and next sampling; and

Qr is the annual water volume.

When the results recorded were less than the limits of detection (LOD) the following estimate of the concentration has been used: Estimated concentration = ((100%-A) • LOD)/100

Where A = percentage of samples below LOD. This procedure is in accordance with OSPAR Agreement 2014:04 (the updated RID Principles). According to these principles (http://www.ospar.org/documents?d=33689) no more than 30% of the samples should be below the LOQ). In 2019, Hg and Ag did not reach this requirement, which was also the case in 2017 (Kaste et al., 2018) and 2018 (Gundersen et al., 2019).

2.4 Trend analyses and data comparison

Trend analysis has been conducted both for weather data (air temperature, precipitation, and water temperature) and the water chemical parameters covered in the monitoring programme. Since stations can be different from what is described in chapter 1.2 for water sampling, trends in weather data are presented together with information on stations and time ranges used. Details for water chemistry and water discharge trend analysis are given below. The general trend analysis methodology described is applied also for the weather data.

When monitoring data collected in 2019 is presented, results are compared with the preceding five- year mean (2014-2019). 2019 data is presented as annual mean (based on monthly values) ± one standard deviation, while 2014-2018 data is presented as 5-year mean (based on annual values) ± one standard deviation.

2.4.1 Trend analysis methodology Trend analyses in this report describe overall loads to the sea but are less suited to discuss changes in upstream sources, because inter-annual variability in water discharge strongly affects fluxes and might therefore mask changes in source emissions. The Mann-Kendall test (Hirsch and Slack, 1984) has been used to test for monotonic trends (including linear trends; Sen slope) in annual riverine inputs and concentrations. Trends are regarded as statistically significant at the 95% significance level (p < 0.05, double-sided test).

2.4.2 Selection of rivers Trend analysis for water chemical parameters was conducted for nine of the former “main rivers” where monthly monitoring data was available since 1990 (Table 8). The remaining two rivers included as “main rivers” in the former RID programme, Alna and Vosso, did not have enough years

NIVA 7564-2020 with monthly monitoring. Alna also had a shift in monitoring methodology for water discharge. Storelva was not monitored at the current sampling site during 2004-2016, and only once a year from 1990- 2003. The remaining rivers all had lower than monthly sampling frequency during 1990-2016. Trend analysis for water discharge was conducted for the nine rivers listed in Table 8, and for an additional nine rivers with discharge data (modelled) since 2004.

2.4.3 Selection of parameters and time-periods The water chemical parameters included in the trend analyses were suspended particulate matter (SPM), silica (SiO2), total organic carbon (TOC), total nitrogen (tot-N), ammonium (NH4-N), nitrate (NO3-N), total phosphorus (tot-P), orthophosphate (PO4-P), Cu, Pb, Zn, Cd, and Ni. Trends for the remaining metals have not been calculated due to the combination of a large proportion of the samples having levels below LOQ and changes in the analytical methods during the time period; see Skarbøvik et al. (2010) for details.

Consistent with last year methodology (Gundersen et al., 2019), long-term (1990-2019) and short- term (2004-2019) trend analyses have been conducted for nutrients (SPM, SiO2, TOC, Tot-N, NH4, NO3, tot-P, and PO4), and metals (Cd, Cu, Ni, Pb, and Zn), respectively, depending on data availability. Trend analyses for metals was only short-term because there was a change in the analytical method, leading to better analytical sensitivity. Such a transition, making it possible to detect lower concentrations in the rivers, could result in a false declining trend. Note that the trend analysis for TOC started in 1999 (instead of 1990) for Numedalslågen, Orreelva, Altaelva, Vefsna, and Skienselva, due to infrequent measurement in the early years of the monitoring. The statistical power of the trend analysis decreases when applied to shorter time-series. Hence, for 2019 the following trend analyses have been performed for the nine former “main rivers” (Glomma, Drammenselva, Numedalslågen, Skienselva, Otra, Orreelva, Orkla, Vefsna and Altaelva): - Long-term trends in concentrations and loads for nutrients, SPM, TOC, and silica for the entire monitoring period (1990-2019), as well as water discharge. Long-term trend analysis for TOC for some of the rivers start in 1999. - Short-term trends (2004-2019) in concentrations and loads for metals, as well as water discharge. Note that for metals, the rivers have only been monitored four times per year in 2017, 2018 and 2019.

Table 8. An overview over the rivers, parameters, and historical frequency of measurement for the nine rivers included in the trend analysis.

Sampling frequency (times yr-1) Short name Rivers/parameters Parameters*** 1990-2003 2004-2016 2017-2019 “Monthly Glomma*, Drammenselva*, Nutrient fractions, 12 12 12 monitored Numedalslågen, Skienselva, Otra, SPM, TOC, silicate since 1990” Orreelva, Orkla, Vefsna and Altaelva** -«- -«- Metals 12 12 4 * Rivers Glomma and Drammenselva have often been sampled 16 times per year, or even more frequently (e.g. during the 1995-flood). ** In River Altaelva, the sampling was less frequent during 1990-1998. *** In 1999-2003 samples were analysed at a different laboratory, and for this reason, concentrations of total phosphorus and mercury data in 1999-2003 are excluded from the time series, whereas the loads are modelled. A more detailed overview of excluded data from historical records is given in Skarbøvik et al. (2010).

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2.5 Comparison of metals data with EU WFD quality standards

Samples for metals determination were analysed unfiltered (section 2.2.2) and results cannot be compared directly with the EU WFD environmental quality standards for priority substances and river basin-specific pollutants in freshwater. This requires analyses of filtered samples (Direktoratsgruppen 2018, Table 9) and is a task undertaken in a subset of rivers in the river monitoring programme (results presented in different reports, e.g. Allan et al., 2018 and 2019). Given that unfiltered samples often have higher concentrations (dissolved + particulate fractions) it implies that it is possible to state if the annual mean concentrations are below – but not above - the threshold concentrations. Unfiltered samples were analysed to capture the total metal export to the oceans, in accordance with OSPAR RID. Moreover, by analysing unfiltered samples the recent data can be compared with the long trend series that have been obtained on unfiltered samples, to look for effects from climate change.

Table 9: Threshold concentrations for metals in Norwegian surface waters (annual averages, filtered samples) (Direktoratsgruppen 2018) Metal As Pb Cd Cu Zn Cr Ni Hg

Limit (µg/L) 0.5 1.2 0.081 7.8 11 3.4 4 0.047

Note that the annual mean values for 2019 (and 2017-2018) were based on quarterly samples, whereas the 5-year mean for eleven of the rivers (“main rivers” from the previous RID programme, Table 1) included years with monthly samples (2013-2016). In general, less frequent sampling is associated with higher uncertainty. An exception in the dataset for 2019 is the Hg data, where samples were collected monthly.

2.6 Modelling of contaminants in the Alna river

Coupled catchment-river models can be valuable tools to describe and to synthesize key processes that determine the temporal and spatial variation in hydrology and hydrochemistry in river systems. When successfully calibrated to historical (measured) data, the models can be applied to simulate possible effects of future changes in environmental or climatic factors as well as changes driven by pollution management and control actions.

Contaminants selected for modelling We have applied an integrated catchment model for contaminants (INCA-Tox) to simulate fate and transport of two PCB congeners (PCB-101, PCB-153) and three PAH congeners (Phenanthrene, Fluoranthene, Benzo-a-Pyrene) in the Alna river. These compounds are chosen for their relevance as environmental contaminants and their inclusion in several national and international regulatory documents. The compounds can be present in the environment either as gases or adsorbed to particulate matter in the atmosphere or in soil and water. Environmental conditions and their specific physical-chemical properties determine how they partition across these media. While PAHs are unintentionally produced mostly during incomplete combustions of both biomasses and fossil fuels, PCBs are legacy pollutants banned by the UNEP Stockholm Convention on Persistent Organic Pollutants (POPs) and represent a group of compounds used in the past in several industrial

1 For water with calcium concentration < 16 mg/l. In more alkaline waters, the threshold value is higher (cf. Direktoratsgruppen 2018)

NIVA 7564-2020 applications. Table 10 and 11 provide the relevant physical chemical properties of these compounds used as inputs for the model simulations.

Model description The INCA-Tox model, formerly known as the INCA-Contaminants model (Nizzetto et al., 2016) solves mass balances of water, carbon, sediments and contaminants in the soil-stream-sediment system of catchments and their river networks as a function of climate, catchment characteristics and contaminant properties. When forced with realistic climate and contaminant input data, the model can predict contaminant concentrations in multiple segments of a river network.

The model integrates a hydrological, soil-stream carbon, sediment transport and a contaminant fate model. The underlying modules are derived from those developed within the Integrated Catchment models (INCA) family. These are one-dimensional dynamic catchment models designed for water quality assessment (Whitehead et al., 1998a; Whitehead et al., 1998b). In summary, the discretization of catchment and river structure is provided by determining the order of streams, their length and width between reach points, and by compiling dimensions and characteristics of individual sub-catchments – including soil properties and land use information.

Table 10: Compound-specific physical-chemical properties used in the model simulation.

-1 3 -1 3 -1 -1 Compound MW (g mol ) V (cm mol ) LogKow H (Pa m mol ) ΔUaw (kJ mol ) Source: 1 Source: 1, 2 Source: 2 Source: 2,3 PCB 101 326.4 289.1 5.33 24.1 59.7 PCB 153 360.9 310 6.87 19.8 62.8 Phenanthrene 202.3 199 4.57 3.24 47.3 Fluoranthene 166.2 217 5.22 1.037 38.7 Benzo-a-pyrene 252.3 263 5.91 0.046 50* Sources: 1) Mackay et al. (1992), 2) Li et al. (2003), 3) Bamford et al. (1999). MW=molecular weight, V=molar volume, Know=octanol/water partition coefficient, H= Henry's law volatility constant, Uaw=enthalpy of phase transfer between air and water. *Data not available. This value was chosen as default.

Table 11. Selected environmental half-lives used in model simulation.

Compound Soil (days) Water (days) Sediment (days) PCB 101 2200 2200 2200 PCB 153 2200 2200 2200 Phenanthrene 240 21 730 Fluoranthene 730 21 730 Benzo-a-pyrene 730 60 2200 Source: Mackay et al. (1991).

The new INCA-Tox model is implemented in the Mobius framework (Norling et al., in review) and is open source, available from https://github.com/NIVANorge/Mobius.

It consists of the following sub-modules:

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• PERSiST - (the Precipitation, Evapotranspiration and Runoff Simulator for Solute Transport) is a hydrology model designed for working with the INCA family of water quality models. It computes water balance, and flow between compartments (Futter et al., 2014). • INCA-Sed - a sediment mobilisation and transport model (Lazar et al., 2010). • INCA water temperature - a very simple water temperature model used to compute temperatures in the river based on air temperatures. • INCA soil temperature - a very simple soil temperature model using air temperature drivers. (Rankinen et al., 2004). • INCA-Tox-C - a simple soil organic carbon (SOC) and dissolved organic carbon (DOC) model used to drive transport of contaminants bound in carbon through DOC that flows with the water and SOC that are mobilised in the sediment transport. • INCA-Tox - The model that computes contaminant partitioning between compartments, degradation, and transport. The contaminant fate module uses temperature and compound physical chemical data to calculate the instantaneous contaminant distribution across the masses of water, carbon and sediments, and determine the fluxes of contaminants moving in the environment associated to these compartments.

While the model does not explicitly describe an atmospheric compartment, it accepts information on contaminants atmospheric concentrations and deposition fluxes. Atmosphere can serve both as a source of pollutants for the catchment environment, or as a recipient of volatilizing compounds from soil and water. A full description of the mathematical frame underpinning INCA-Contaminants is provided by Nizzetto et al. (2016). The same frame is utilized by the new version of the model (INCA- Tox), with some simplification introduced in the description of the environment.

Time series input data and boundary conditions for atmospheric and soil contamination. Temperature and precipitation data were derived from the Nordic Gridded Climate Dataset: https://thredds.met.no/thredds/catalog/ngcd/catalog.html which yields a 1 km grid that was then area-averaged to the Alna basin. Wind speeds were taken from the Blindern meteorological station. Temperature and precipitation are the drivers of the hydrological model, while temperature also drives changes in the partitioning of contaminants between compartments. Wind speed drives the air-water contaminant exchange in the river phase.

For atmospheric contaminant concentrations we used constant values based on averages of NILU time series data from the Birkenes station in southernmost Norway (data available at http://ebas.nilu.no/). Atmospheric deposition of the pollutants included wet, dry and litterfall- associated deposition. Litterfall-associated deposition was estimated by considering published yearly averaged deposition velocities (Horstmann and Mclachlan, 1998) multiplied by air concentrations extracted from the EBAS dataset. Daily wet deposition inputs were generated by assuming equilibrium between air and rain/snow combined with rainfall and snowfall measurements.

The boundary conditions were further refined by considering data of soil contamination available for forested areas surrounding Oslo generated by NIVA in 2015. To refine the atmospheric deposition scenario (which were based on data from the rural background station Birkenes) we computed realistic wet and dry depositions based on the atmospheric concentrations, then added in a third artificial deposition to maintain a constant PCB and PAH budget in the soil. Soil concentration data used to set the boundary condition of INCA-Tox are summarised in Table 12. The underlying assumption for setting atmospheric deposition to a level that maintain stationary soil contamination

NIVA 7564-2020 stems from the consideration that the level of PCBs and PAHs in soils vary slowly over time, due to the persistence to degradation and the high affinity for soil organic matter (Schuster et al., 2011).

Table 12. Boundary conditions for catchment soil contamination.

Compound Concentration in soil (ng/g soil OC) min max PCB 101 0.2 2.2 PCB 153 0.2 7.6 Phenanthrene 19.7 316.2 Fluoranthene 6.2 648.4 Benzo-a-Pyrene 5.2 350 Source: Soil contamination survey 2015 in forested areas surrounding Oslo.

Measured data used for calibration Discharge data used for calibration of the hydrological model was obtained using NVE's Hyd API (https://hydapi.nve.no/UserDocumentation/). Measured data 2012-2018 on suspended sediments (SS), DOC and TOC were from the Norwegian River Monitoring programme. The contaminant data were collected by the same programme between 2011 and 2016 (Skarbøvik et al., 2014; 2015; 2016; 2017).

2.7 Sensor monitoring in Rivers Storelva and Målselva

The rivers Storelva and Målselva sensor stations are located at the same sites as the manual sampling sites (Table 1). Water from the river is pumped a few meters to an instrument container with flow cells equipped with sensors that measure water temperature, pH, conductivity, turbidity and fluorescent dissolved organic matter (FDOM). Data are recorded on an hourly basis, transferred to NIVA’s server and made available online at www.aquamonitor.no/LandSjo/.

Water flow data are obtained from the Norwegian Water Resources and Energy Directorate (NVE) real-time stations 18.4.0. Lundevann, which is located close to the NIVA station in River Storelva, and 196.35.0 Målselvfossen, which is located 15 km upstream of the NIVA station in River Målselva.

A QA routine has been set up by flagging data that are obviously wrong, due to e.g. interrupted power supply, clogging, etc. Flagged data are not visible online or downloadable but are kept in the database. The sensors need repeated inspection during the year, and the stations are visited at regular intervals for service and maintenance. Temperature correction of the FDOM data for River Storelva was done in accordance with Ryder et al. (2012). The intercept constant was set to 100, and the slope intercept was chosen as to give the best correlation between temperature corrected FDOM and dissolved organic carbon (DOC) concentration for the time period 2015-2018. In River Målselva, temperature correction of FDOM was omitted as it did not improve the fit with DOC.

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3. Results and Discussions

3.1 Climate and hydrology: status and trends

3.1.1 Air temperature and precipitation in 2019 The 2019 average air temperature for Norway (Figure 4, left) was 1.2 oC above the 1961-1990 normal and was the 20th warmest year since the measurements started in 1900. The average air temperature was above the normal during winter (+2.3 oC), spring (+1.4 oC), and summer (+1.2 oC), while in autumn, the temperature was slightly below (-0.6 oC) the normal. Overall geographical variation constituted temperatures above the normal (up to +2 oC above) at the Southern parts of the country, while temperatures were slightly below the normal furthest north. This was a result of a warmer-than normal winter in the south (up to +4 oC above the normal) and a colder than normal autumn in the north (- 1.5-3 oC). In 2019, a new national record was set for the highest minimum daily temperature, measured to 26.1 oC the 28th of July at Sømna – Kvaløyfjellet (Nordland). A total of 18 county temperature records were set: 5 cold and 13 warm (pre-2020 county division).

Air temperature in Precipitation in 2019 2019 (deviation from (deviation from the the 1961-1990 normal) 1961-1990 normal)

Figure 4: Air temperature (left) and precipitation (right) in Norway in 2019 as deviation from or percentage of the normal values (1961-1990). Maps edited from Grinde et al. (2020).

Precipitation in 2019 (Figure 4, right) was a little higher than the normal (115%) for the country, constituting the 15th wettest year since measurements started in 1900. Precipitation was higher than normal during winter (120%), spring (130%), and summer (110%), while in autumn, precipitation was slightly lower than normal (95%). Geographically, the western part of the country received less precipitation than normal most of the year (50-70%), except from during summer when the

NIVA 7564-2020 precipitation was higher than normal (150-200%). Eastern and northern parts of the country received higher than normal precipitation during most of the year. For more details on the weather in 2019, we refer to Grinde et al. (2020).

3.1.2 Trends in air temperature and precipitation 1980-2019 Table 13 shows trends in air temperature and precipitation since 1980 (1981 for Vosso and 1983 for Drammenselva) at meteorological stations located in the near vicinity of the river monitoring sites. The results show a significant increase in air temperature at nearly all the stations. For precipitation, only the station near Altaelva and Storelva showed a significant trend of increase. Large year-to-year variation in precipitation could potentially explain the lack of significant trends. These results were in accordance with the findings from 2018 (Gundersen et al., 2019).

Table 13. Trends in air temperature and precipitation 1980-2019. Data from the Norwegian Meteorological Office (met.no).

Temperature Precipitation

River name St.no Years Temp. Temp St. no Years Precip. Precip. trend change trend change (p-value) (°C)* (p-value) (mm)*

Glomma SN700 1980-2019 0.008 +1.3 SN3780 1980-2019 0.139 +126

Alna SN18700 1980-2019 0.000 +1.7 SN18700 1980-2019 0.067 +141

Drammenselva SN19710 1983-2019 0.027 +1.0 SN19710 1983-2019 0.147 +122

Numedalslågen SN27470 1980-2010 1.00 +1.9 SN30000 1980-2019 0.877 -8

Skienselva SN27470 1980-2010 1.00 +1.9 SN30260 1980-2015 0.149 +133

Storelva SN36560 1980-2018 0.000 +1.4 SN36560 1980-2019 0.024 +320

Otra SN39040 1980-2019 0.001 +1.2 SN39040 1980-2019 0.147 +228

Bjerkreimselva SN44560 1980-2019 0.000 +1.4 SN43360 1980-2017 0.365 +122

Orreelva SN44560 1980-2019 0.000 +1.4 SN44190 1980-2019 0.712 +41

Vikedalselva SN46910 1980-2011 0.003 +1.6 SN46850 1980-2019 0.099 +453

Vosso SN52290 1981-2007 0.026 +1.4 SN51250 1980-2019 0.393 +295

Nausta SN58070 1980-2017 0.004 +1.1 SN57480 1980-2019 0.507 +148

Driva SN64550 1980-2007 0.003 +1.7 SN63530 1980-2019 0.477 -62

Orkla SN69100 1980-2018 0.011 +1.1 SN66210 1980-2009 0.915 +46

Nidelva SN69100 1980-2018 0.011 +1.1 SN68270 1980-2019 0.345 +91

Vefsna SN85380 1980-2018 0.000 +1.5 SN78850 1980-2007 0.244 +316

Målselva SN89350 1980-2019 0.091 +0.8 SN89350 1980-2019 0.301 +53

Altaelva SN93140 1980-2019 0.000 +1.4 SN93140 1980-2017 0.024 +119

Tana SN96800 1980-2012 0.008 +1.8 SN96970 1980-2018 0.798 +16

Pasvikelva SN99370 1980-2019 0.000 +1.8 SN99500 1980-2018 0.406 +24

Red – significantly increasing trend, p<0.05. There were no significantly decreasing trends. * Change in temperature and precipitation is the total change for the whole period.

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3.1.3 Water temperature – status 2019 and trends Generally, water temperatures show a strong seasonal pattern (Figure 5) and vary from the north to the south. Patterns for 2019 are very similar to those of 2018 (Gundersen et al., 2019). Unfortunately, the TinyTag logger was lost from Orreelva and data does not exist for the last 6 months of 2019 (Table 14).

The stations included in the long-term water temperature trend analysis are given in Table 15 and with details on the time series in Table 4. Note that eight of the rivers have not been included since they either do not have a temperature station nearby or the available long-term data series is incomplete. 2019 data had not been made available by the time of the data analysis (marked by “*” and text in grey) for eight of the stations (Table 15). For the remaining four rivers the inclusion of the 2019 water temperature did not lead to any major changes in the trends. Four rivers, including the two northern rivers Altaelva and Pasvikelva, displayed significantly increasing trends in water temperature, which agrees with the 2017 and 2018 results (Gundersen et al., 2019; Kaste et al., 2018).

Table 14. Monthly water temperature measured in the monitored rivers in 2019

River name Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Glomma 0.4 0 1.4 2.8 7.8 11.4 16.4 19.8 15.3 9.3 4.6 1.2 Alna 2.9 0.7 3.1 4.1 7.3 12.3 11.6 14.4 14.3 9.4 3.4 3.1 Drammenselva 1.4 0 1.5 2.7 7.5 10.7 16.9 20.7 16 9.2 6.1 2.4 Numedalslågen 0.0 0.1 0.9 4.7 8.6 13.8 18.8 17.8 12.7 7.0 1.9 0.3 Skienselva 3.3 1.9 2.4 3.6 5.9 10.5 15.7 16.8 14.8 10.4 6.7 4.4 Storelva 2.4 2.5 3.3 7.4 13.8 17.4 21.7 20.9 14.8 9.7 5.7 3.8 Otra 1.4 1.2 2.4 5.9 10.2 13.2 18.0 17.9 14.1 9.1 4.5 3.1 Bjerkreimselva 4.9 1.8 4.1 6 10.1 - 16.4 20 15.6 11.8 - 5.2 Orreelva 3.2 3.9 5.4 9.2 11.5 12.9 ------Vikedalselva 2.0 2.2 3.3 6.1 9.5 13.0 16.7 16.3 12.0 8.3 4.1 4.0 Vosso 2.1 1.7 2.1 3.0 - 10.4 14.3 15.8 11.2 8.3 5.2 3.5 Nausta 1.3 -0.2 1.5 2.2 6.4 9.1 11.9 14.7 13.1 2.9 0.0 2.1 Driva 4.0 1.6 2.6 3.0 3.0 3.5 8.0 16.5 14 6.0 1.0 2.0 Orkla 0.4 1.0 1.2 2.4 5.6 11.2 11.5 11.6 8.2 3.1 0.9 0.3 Nidelva 3.4 1.5 2.2 2.2 3.9 5.3 9.0 15.6 15.4 8.8 2.0 4.3 Vefsna 0.0 0.1 0.1 2.3 4.5 8.4 13.9 14.3 8.9 2.8 0.3 0.0 Målselva 0.0 0.0 0.0 0.1 4.4 7.5 11.9 13.1 8.5 1.7 0.1 0.1 Altaelva 0.0 0.1 0.1 0.3 3.4 5.8 9.5 11.3 10.1 4.3 0.8 0.4 Tana 0.1 0.0 0.1 0.3 3.8 4.1 12.6 4.0* 5.6 3.0 2.3 Pasvikelva -0.1 0.0 0.0 0.0 0.4 6.7 12.8 11.6 15.3 5.9 3.0 3.6 *Average temperature measured in Tana in August is lower than expected. Previous year’s data typically show higher temperature in August compared to September. The reason for the deviation is not known.

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Figure 5: Monthly measured temperature in the rivers. Note that the shade of the lines indicates geographical location (South-eastern rivers are darker while the northern are the lightest). Data presented in Table 14.

Table 15. Trends in annual mean water temperature, in rivers with available long-term data.

River name Years with data Annual change** p-value

Drammenselva 22 0.04 0.028 Numedalslågen 13* 0.03 0.760 Skienselva 23 0.01 0.091 Otra 24* 0.04 0.503 Bjerkreimselva 28* 0.06 0.019 Vikedalselva 30 0.06 0.080 Vosso 20* 0.04 0.496 Nausta 15* <0.01 0.276 Orkla 23 <0.01 0.224 Altaelva 25* 0.04 0.001 Tana 16* 0.03 0.558 Pasvikelva 22* 0.05 0.018

Red – significantly upward p<0.05. There were no significantly decreasing trends.

*data from 2019 had not been made available at the time of analysis. **annual change in °C calculated as [total temperature change]/[(last year of data)-(first year of data)]

3.1.4 Water discharge – status 2019 and trends Comparing data from 2019 with the preceding five years (2014-2018, Figure 6), water discharge was lower in the southern and western rivers – from Otra to Nausta. This coincided with lower precipitation

NIVA 7564-2020 in 2019 for the western region of the country (50-70 % of 1961-1990 normal, Figure 4). For the eastern and northern rivers, 2019 data were within the range of the preceding five years (Figure 6), despite the fact that eastern and northern parts of Norway received higher than normal precipitation during most of the year (Figure 4).

In 2019, the geographical variation in water discharge followed the same patterns as for the previous five years: in southern Norway the water discharge increased when going from east to the west (from Glomma to Nausta), in middle Norway it decreased from south to north (from Driva to Vefsna), and in northern Norway it decreased from south-west to north-east (Målselva to Pasvikelva) (Figure 6).

Figure 6: Average annual water discharge for the five preceding years (2014-2018, orange) and annual water discharge in 2019 (blue) for the monitored rivers. Error bars illustrate interannual variation in the five-year mean (± stdev).

Long-term trend analysis (1990-2019) of water discharge for the rivers with monthly monitoring since 1990 is presented in Table 16. Significant increasing trends were found for the south-eastern rivers Glomma and Drammenselva. Levels have increased by 5 and 16 %, for Glomma and Drammenselva, respectively, when comparing annual average in 1990 and 2019. Although the precipitation increases for Glomma (+126 mm) and Drammenselva (+ 122 mm) between 1980 and 2019 are not significant (p > 0.05, Table 9), other studies have found an annual national increase in precipitation of 18% since year 1900 (MET, 2015).

In last year’s monitoring report, a significantly increasing trend in water discharge was also observed for Skienselva (Gundersen et al., 2019) which is not the case this year (Table 16). Among the rivers where water discharge data was available from 2004, only Tana showed a significantly increasing trend. This is in accordance with the findings in last year’s report (Gundersen et al., 2019). It should be note that 13 rivers included in the program, Glomma, Drammenselva, Numedalslågen, Skienselva, Otra, Bjerkreimselva, Driva, Orkla, Nidelva, Vefsna, Målselva, Altaelva, and Pasvikelva are regulated. This will affect water discharge data and may clarify why discharge changes are not necessarily explained by

NIVA 7564-2020 precipitation data. Also, precipitation data does not cover the whole catchment, which complicates the comparison to discharge data.

Table 16. Trends in annual water discharge. Showing p-values

Long-term Annual change Short-term Annual change River River 1990-2019 (m3/s)* 2004-2019 (m3/s)*

Glomma 0.030 4.5 Bjerkreimselva 0.344 0.2 Drammenselva 0.010 3.0 Vikedalselva 0.822 0.0 Numedalslågen 0.164 0.7 Vosso 0.753 0.2 Skienselva 0.116 1.8 Nausta 0.620 -0.1 Otra 0.392 0.4 Driva 0.300 -0.8 Orreelva 0.074 0.0 Nidelva 0.224 -1.1 Orkla 0.521 -0.2 Målselva 0.224 0.7 Vefsna 0.318 -0.9 Tana 0.003 4.8 Altaelva 0.335 0.2 Pasvikelva 0.053 2.9

Red – significantly increasing p<0.05. There were no significantly decreasing trends *Change shown as m3/second/year

3.2 Water quality status 2019

The Norwegian river monitoring programme is designed so that the results can be used for classification of ecological and chemical status according to the principles in the EU WFD. Thresholds for achieving good ecological status for individual quality elements (and underlying parameters) and good chemical status are given in the Norwegian classification guidance (Direktoratsgruppen 2018). Throughout this chapter the results will be evaluated with respect to these thresholds. The classification is only relevant for the water body where the monitoring site is located (Table 1).

3.2.1 pH and calcium Levels of pH and calcium (Ca) typically covariates in the river water (i.e. elevated Ca concentration gives elevated pH). The 2019 levels of both pH and Ca were within the annual variation of the five and two preceding years, respectively, for all the rivers (Figures 7 and 8). For details on the geographical variation of pH and Ca concentrations and a classification based on the Norwegian typology for the WFD, we refer to last year’s monitoring report (Gundersen et al., 2019).

Note that Ca is a relatively new parameter in the river monitoring programme, it was introduced in 2017.

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Figure 7: 5-year average for pH (2014-18, orange) and annual average for 2019 (blue) for the monitored rivers. Error bars illustrate interannual variability (± stdev) for the 5-year mean and intra- annual variability for the 2019 mean (± stdev). Mean values and standard deviation are based on pH values, not the H+ concentration. This represents a negligible error when pH values are above 6.0.

Figure 8: 2-year average annual calcium concentration (2017-18, orange) and annual average for 2019 (blue) for the monitored rivers. Error bars illustrate interannual variability (± stdev) for the 2- year mean and intra-annual variability for the 2019 mean (± stdev).)

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3.2.2 Suspended matter (turbidity, SPM, and silica) Turbidity is an optical measure of material in the water that can scatter light. Turbidity covers both SPM (0.4 µm < SPM < 2 µm) and colloidal material (<0.4 µm, e.g. SiO2). These parameters are important for the water quality by influencing processes such as light penetration and transport of metals/nutrients.

The 2019 data shows that Orreelva has much higher turbidity and SPM concentrations than the other rivers included in the monitoring program (Figure 9 and 10). In addition to be a river influenced by agriculture (Table 2), it is also influenced by easily erodible clays. Glomma and Alna are also rivers with typically high levels of suspended matter, but both turbidity and SPM concentrations were lower in 2019 compared to the previous five years. For the other rivers with lower concentrations, levels were more similar to historical data (Figure 9 and 10).

It is worth noting that both SPM and turbidity are strongly influenced by seasonal precipitation events, providing highly variable data throughout the year.

Figure 9: Average annual turbidity for the five preceding years (2014-18, orange) and average for 2019 (blue) for the monitored rivers. Error bars illustrate interannual variability (± stdev) for the 5- year mean and intra-annual variability for the 2019 mean (± stdev).

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Figure 10: Average annual suspended particulate matter (SPM) concentration for the five preceding years (2014-18, orange) and average for 2019 (blue) for the monitored rivers. Error bars illustrate interannual variability (± stdev) for the 5-year mean and intra-annual variability for the 2019 mean (± stdev).

SiO2 is a major component of sand and clay and can therefore enter surface water through erosion. It is an essential nutrient for diatoms, which is an important phytoplankton group. Thus, changes in levels of SiO2 can, together with nutrient information, provide an indication on potential eutrophication. Typically, low SiO2 concentrations (Figure 11) are similar to Ca (Figure 8) and are found in areas with slow-weathering bedrock, which is typical for southern and western parts of Norway. The 2019 SiO2 levels consistently followed those of the 5-years means.

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Figure 11: Average annual silica (SiO2) concentration for the five preceding years (2014-18, orange) and average for 2019 (blue) for the monitored rivers. Error bars illustrate interannual variability (± stdev) for the 5-year mean and intra-annual variability for the 2019 mean (± stdev).

3.2.3 Organic carbon Overall, the 2019 levels of TOC were within the annual variation of the five preceding years (Figure 12) and followed the typical geographical pattern that was evident in last year’s data (Gundersen et al., 2019). Briefly, the highest levels are found in the south-eastern rivers where catchments are dominated by forests (e.g. Glomma, Drammenselva, Numedalslågen) and the lowest levels in the western rivers where catchments typically have thin soils and exposed bedrock (e.g. Vikedalselva, Vosso, Driva) (Figure 2).

There was, like last year (Gundersen et al., 2019), one exception from this pattern: Orreelva. In Orreelva, concentrations were high compared to both geographical location and the five-year mean. As discussed by Gundersen et al. (2019), the river has effluent inputs and diffuse water discharge from agriculture that likely contribute to the elevated TOC concentrations. See also the discussion of data on particulate OM below.

According to the Norwegian WFD typology and data collected in 2019, Bjerkreimselva, Vikedalselva, Vosso, Vefsna, Driva and Målselva can be characterized as having very clear water (TOC < 2mg/L). Similarly, Glomma, Alna, Drammenselva, Numedalslågen, Skienselva, Otra, Nausta, Orkla, Nidelva, Altaelva, Tana and Pasvikelva were clear (2 mg/L < TOC < 5 mg/L), whereas Storelva and Orreelva were humic (TOC > 5 mg/L).

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Figure 12: Average concentration of total organic carbon for the five preceding years (2014-18, orange) and average for 2019 (blue) for the monitored rivers. Error bars illustrate interannual variability (± stdev) for the 5-year mean and intra-annual variability for the 2019 mean (± stdev).

Comparing the mean annual concentrations of dissolved and particulate organic carbon in the rivers (Figure 13), it is evident that TOC largely consists of the dissolved fraction (> 90% DOC). Again, Orreelva is the outlier compared to the remaining dataset of rivers, with the lowest dissolved fraction (74% DOC). This reflects the high turbidity and SPM levels recorded for Orreelva in 2019 (Figure 9 and 10).

Figure 13: Average particulate (POC, light blue) and dissolved (DOC, dark blue) organic carbon concentration for 2019 in the monitored rivers. Note that the sum of POC and DOC equals to TOC.

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Any deviation from the TOC in Figure 12 result from analytical uncertainties in the individual methods used to determine POC and DOC.

3.2.4 Nutrients Phosphorus Excess input of phosphorus (P) is regarded as the main driver for eutrophication in Norwegian water bodies. Major sources include agricultural activities, water discharge from urban areas, and weathering of P-containing minerals (e.g. apatite). In the river water, the bioavailability of P will mainly depend on its chemical form and whether it is bound to particles or freely dissolved in the water.

Similar to last year, concentrations of total P are much higher in Alna and Orreelva compared to the other rivers (Figure 14) which is consistent with the fact that they are highly affected by urban and agricultural activities, respectively. Annual mean 2019 tot-P concentrations are very similar to the 2018 concentrations (Gundersen et al., 2019) and both rivers have less than good ecological status according to the good/moderate boundary for tot-P (Direktoratsgruppen, 2018). Of the remaining rivers, only Glomma and Numedalslågen had elevated tot-P concentrations. Glomma and Numedalslågen were still below the good/moderate boundary.

Figure 14: Average concentration of total phosphorous (tot-P/TOTP) for the five preceding years (2014-18, orange) and average for 2019 (blue) for the monitored rivers. Error bars illustrate interannual variability (± stdev) for the 5-year mean and intra-annual variability for the 2019 mean (± stdev). Note the different y-scale range on the right-side panel.

It is reasonable to assume that the large variation in tot-P concentrations (Figure 14) was associated with particle bound phosphorus transported to the rivers in connection with seasonal discharge events. In fact, most rivers had a relatively high proportion of particulate-P (Figure 15), and especially for Glomma and Orreelva which had high SPM concentrations (Figure 10). When bound to particles, the bioavailability of P is reduced.

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Figure 15: Distribution of the 2019 average concentration of total dissolved (TDP, light blue)- and total particulate phosphorus (TPP, dark blue) in the monitored rivers. Note that the sum of TDP and TPP equals to tot-P. The TPP was calculated as the difference between tot-P and the TDP. Note the different y-scale range on the right-side panel.

Phosphate (PO4) is an inorganic form of tot-P which is easily available for algae and other primary producers. The highest annual mean PO4 concentration was found in Alna (50 µg/L) and Orreelva (19 μg/L, Figure 16), likely resulting from the catchment activities previously mentioned.

Figure 16: Distribution of the 2019 average concentration of inorganic (light blue)- and organic phosphorus (dark blue) in the monitored rivers. The organic P fraction has been calculated as the

NIVA 7564-2020 difference between tot-P and phosphate and can also include tightly bound inorganic P. Note the different y-scale range on the right-side panel.

Nitrogen The major sources for nitrogen (N) in river basins are water discharge from agriculture, atmospheric deposition, scattered dwellings, urban wastewater, and diffuse water discharge from upland areas. As for P, N can also exist in different chemical forms and be either freely dissolved or associated with particulate material.

The same two rivers, as for tot-P, stand out with higher total N (tot-N) concentrations (Alna 1500 and Orreelva 1480 μg/L, Figure 17). As for previous years, the concentrations exceeded the good/moderate boundary for tot-N for their respective water types (Direktoratsgruppen 2018). Relatively high levels of tot-N (> 300 µg/L) were also evident for several of the rivers in the south and south-western part of Norway (e.g. Bjerkreimselva, Vikedalselva, Storelva). This is likely an effect of atmospheric deposition (Garmo and Skancke 2018, Gundersen et al., 2019).

Figure 17: Average annual concentrations of total nitrogen (TOTN/tot-N) for the five preceding years (2014-2018, orange) and the 2019 average (blue) for the monitored rivers. Error bars illustrate interannual variability (± stdev) for the 5-year mean and intra-annual variability for the 2019 mean (± stdev).

Two forms of nitrogen are available for plant uptake, nitrate (NO3) and ammonium (NH4). NO3 is the dominant fraction of tot-N in Norwegian surface waters, except in humic waters where the organically bound-N (total organic nitrogen, TON) can dominate. Similar to the findings in last year’s report (Gundersen et al., 2019), the highest relative NO3 content, i.e. 76% of tot-N, was found in Bjerkreimselva, whereas Tana and Pasvikelva had the lowest relative NO3 contents: 28% and 24%, respectively (Figure 18). Ammonium (NH4) concentrations are typially low in Norwegian surface waters, except if sites are highly polluted or have low oxygen content. Unsurprisingly, the highest NH4 levels were found in Alna and Orreelva.

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Figure 19 displays the concentrations of particulate and dissolved fractions of nitrogen in the rivers and highlights that nitrogen has less affinity to particles than phosphorus, as shown in Figure 15. The rivers with high particle content, Alna and Orreelva, had the highest concentrations of particulate N.

Figure 18: Distribution of the 2019 average concentration of ammonium-N (light blue), nitrate-N (blue), and organic-N in the monitored rivers. The organic-N fraction was calculated as the difference between tot-N and the two inorganic fractions (ammonium-N and nitrate-N).

Figure 19: Distribution of the 2019 average concentration of total particulate-N (TPN, light blue) and dissolved-N (TDN, dark blue) in the monitored rivers. The TDN was calculated as the difference between tot-N and TPN.

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3.2.5 Metals Arsenic (As) In 2019, all reported As concentrations were below the EU WFD environmental quality standard threshold level (Table 9). Alna (0.38 µg/L) and Orreelva (0.38 µg/L) had the highest annual mean As concentrations, followed by Storelva (0.26 µg/L) and Vikedalselva (0.26 µg/L). For Alna, Storelva and Vikedalselva mean annual concentrations were similar to the preceding fiver year mean, while Orreelva had elevated concentrations compared to the historical data (Figure 20). Pasvikelva had lower mean annual concentration (0.14 µg/L) than the 2014-2018 mean, while Numedalslågen were higher (0.20 µg/L). Of the remaining rivers, all annual means were below 0.2 μg/L (Figure 20). The elevated As levels in Alna and Orreelva can be explained by the high local anthropogenic influence on these rivers. Pasvikelva is likely influenced by pollution from the large metallurgical complex (Nornickel, Russia) located in close vicinity to the river. Higher metal concentrations in Storelva may be linked to former mining and smelting industries within the catchment. Transport of metals to the surface waters may also to a larger extent be facilitated by DOM in this river, given the relatively high content of OC (TOC: 6.0 mg C/L, DOC: 5.8 mg/L). Given the relatively high particle content in Alna (Figure 9-11), the dissolved fraction of As is likely to be low. For Pasvikelva, on the other hand, the content was low and thus the occasionally high As concentration gives more reason for concern.

Figure 20: Average annual concentration of arsenic for the five preceding years (2014-2018, orange) and average for 2019 (blue) for the monitored rivers. Error bars illustrate annual variation for the 5- year mean (± stdev) and monthly variation for the 2019 mean (± stdev).

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Lead (Pb) Annual mean concentrations of Pb in 2019 (Figure 21), showed more or less the same pattern as the previous monitoring year (Gundersen et al., 2019). None of the rivers showed Pb levels exceeding the threshold concentration (1.2 μg/L, Table 9). Mean annual 2019 concentrations were generally lower than the 5-year means, and especially for the high-concentration rivers such as Alna (0.47 μg/L), Drammenselva (0.06 μg/L), and Storelva (0.37 μg/L). Two exceptions were Numedalslågen (0.45 μg/L) and Orreelva (0.35 μg/L), where levels were higher than the five-year mean.

It is worth mentioning that the apparent pattern of declining Pb concentrations could be an artefact from the less frequent measurements conducted between 2017 and 2019 compared to the previous years (from monthly to quarterly). With less frequent measurements, pulses of elevated Pb concentrations, caused by for example increased particle content, might have gone unnoticed.

Figure 21: Average annual concentration of lead for the five preceding years (2014-2018, orange) and average for 2019 (blue) for the monitored rivers. Error bars illustrate annual variation for the 5- year mean (± stdev) and monthly variation for the 2019 mean (± stdev).

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Cadmium (Cd) Alna (0.03 μg/L), Storelva (0.04 μg/L), and Orkla (0.03 μg/L) had the highest annual mean cadmium (Cd) concentrations (Figure 22). In Orkla, this is likely resulting from water discharge from an abandoned Cu mine in the catchment (Gundersen et al., 2019). For Alna, the trend of annual mean Cd concentrations around 50% of the 5-year mean was continued (Gundersen et al., 2019). As has been observed in previous years, Pasvikelva was the only river with elevated concentrations in the northern parts of the country. Likely due to airborne pollution from the industry on the Russian side of the border. The remaining rivers had low Cd concentrations, and all measurements were below the threshold concentration (0.08 μg/L, Table 9).

Figure 22: Average annual concentration of cadmium for the five preceding years (2014-2018, orange) and average for 2019 (blue) for the monitored rivers. Error bars illustrate annual variation for the 5-year mean (± stdev) and monthly variation for the 2019 mean (± stdev).

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Copper (Cu) In 2018, the highest annual mean Cu concentration was found in the northern river Pasvikelva (10.6 µg/L, Gundersen et al., 2019). Gundersen et al., 2019 argued that, based on low content of SPM and TOC in Pasvikelva, a large fraction of the Cu resides in the dissolved form, and it was likely that the threshold level (7.8 µg/L, Table 9) was exceeded. In 2019, the annual mean Cu concentrations in Pasvikelva is much lower (4.0 µg/L), also below the threshold level. The concentrations measured in 2019 is more in line with the 5-year mean (Figure 23). 2018 could represent an outlier in the dataset, but the river catchment does receive air-pollution from the metallurgical complex located in close vicinity on the Russian side of the border.

Orkla had the highest annual mean Cu concentration (4.8 µg/L) in 2019, but all remaining rivers had levels below the threshold concentration.

Figure 23: Average annual concentration of copper for the five preceding years (2014-2018, orange) and average for 2019 (blue) for the monitored rivers. Error bars illustrate annual variation for the 5- year mean (± stdev) and monthly variation for the 2019 mean (± stdev).

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Zinc (Zn) Like the previous year of monitoring, the highest annual mean Zn concentration was observed for Orkla (10 μg/L, Figure 24) and Alna (10 μg/L). Assuming parts of the Zn are particle bound, the dissolved concentration of Zn is likely to be below the threshold value (11 μg/L, Table 9). The three rivers with the highest 5-year mean, Alna, Orkla, and Glomma, had all much lower concentrations in 2019 compared to the historical data. This is a continuation of a pattern observed the last years and is likely due to the reduced sampling frequency since 2017. Metals are typically transported with particles, and particles generally show a high monthly variation.

Figure 24: Average annual concentration of zinc for the five preceding years (2014-2018, orange) and average for 2019 (blue) for the monitored rivers. Error bars illustrate annual variation for the 5-year mean (± stdev) and monthly variation for the 2019 mean (± stdev).

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Chromium (Cr) Mean annual Cr concentrations were low (<0.5 μg/L, Figure 25) for all rivers and below the threshold concentration (3.4 μg/L, Table 9). For the rivers with the highest five-year mean concentrations, Glomma, Alna, and Orkla, the 2019 concentrations were lower than the historical data.

Figure 25: Average annual concentration of chromium for the five preceding years (2014-2018, orange) and average for 2019 (blue) for the monitored rivers. Error bars illustrate annual variation for the 5-year mean (± stdev) and monthly variation for the 2019 mean (± stdev).

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Nickel (Ni) Mean annual Ni concentrations are clearly higher in Pasvikelva compared to the other monitored rivers (Figure 26). The annual mean in Pasvikelva was 9.3 μg/L in 2019, a concentration approximately 8 times higher than the second highest observed annual mean (1.2 μg/L for Orreelva). For all rivers, including Pasvikelva, the 2019 data were similar to the five-year mean (Figure 26).

Of the monitored rivers, it is only for Pasvikelva that the mean annual concentration is higher than the EU WFD threshold value (4 μg/L, Table 9). In Pasvikelva, SPM and TOC concentrations were low, and it is likely that the threshold concentration was exceeded. It is not surprising that the Ni levels are high in Pasvikelva. Contamination in Pasvikelva results from heavy influence from the Norilsk nickel plant, located a few kilometres away on the Russian side of the border.

Figure 26: Average annual concentration of nickel for the five preceding years (2014-2018, orange) and average for 2019 (blue) for the monitored rivers. Error bars illustrate annual variation for the 5- year mean (± stdev) and monthly variation for the 2019 mean (± stdev). Note the different y-scale range on the right-side panel.

Mercury (Hg) Previous activities of the Norwegian river monitoring programme have not been able to evaluate river Hg concentrations because a high number of samples had concentrations below analytical detection limits. Data from the past five years are correspondingly incomplete and associated with large uncertainties, see Braaten et al. (2018) for details. As an example, in 2018 70 of 70 samples had Hg concentrations below the LOQ (1 ng/L) (Gundersen et al., 2019). For samples collected in 2019, two methods were used for Hg determination (Section 2.2.2). The method previously used (i.e. the AAS method) resulted in 58 of 80 samples with Hg concentrations below the LOQ (1 ng/L). However, the CVAFS method used on samples collected monthly in 2019 provided 238 data entries of concentrations above the 0.2 ng/L LOQ (Table 6).

Hg, being very toxic and having a high potential for bioaccumulation, has the lowest threshold level among the metals (0.047 μg/L, Table 9). Here, Hg concentrations were very low for most of the rivers (< 2 ng/L, Figure 27). However, at several sites in the country, the Hg level in fish (both

NIVA 7564-2020 freshwater and marine) exceeds the recommended dietary intake. Hence, a continued effort to determine river Hg concentrations with lower detection limits is needed in order to provide an adequate time series for river water.

Figure 27: Average annual concentration of mercury for 2019 (blue) for the monitored rivers. Error bars illustrate monthly variation for the 2019 mean (± stdev).

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3.3 Additional Rivers - Water quality status 2019

As in 2018 (Gundersen et al., 2019), water chemistry from a selection of rivers from the National monitoring program for limed rivers (Kalkningsovervåkningen) have been included to compliment the picture established by the 20 main river of the programme. Data for 2019 are included for six rivers, Nidelva, Tovdalselva, Mandalselva, Lygna, Suldalslågen, and Ekso (Table 2), compared to ten rivers in 2018 (additional rivers were Sira, Stryneelva, Namsen, and Saltdalselva, Gundersen et al., 2019). The rivers from the National monitoring program for limed rivers are from the south- and southwestern Norway (Figure 2).

The samples have been analysed at a different chemical laboratory (Vestfold lab) than the main river samples (NIVA lab). Since these rivers have not been routinely monitored with fully matched parameter lists, a complete 5-year mean does not exist for comparison. Data are only compared with results from 2018.

3.3.1 pH and calcium Concentrations of pH and Ca showed very little difference compared with data from the previous year (Figure 28). As expected, given the acid-sensitive character of these rivers, levels are lower than what is reported for the main 20 rivers (Figures 7 and 8).

Figure 28: Average annual concentration of pH (above) and calcium in mg/L (below) for 2018 (orange) and for 2019 (dark blue) for the additional rivers included from the National monitoring program for limed rivers programme. Error bars illustrate interannual variability (± stdev). For pH, the average values and standard deviation are based on pH values, not the H+ concentration. This represents a negligible error when pH values are above 6.0.

3.3.2 Suspended matter, (turbidity, SPM, and silica) All rivers had low mean annual SPM concentrations (≤ 1 mg/L, Figurew 29), similar to what was reported last year (Gundersen et al., 20919). Correspondingly, turbidity measurements were also low (≤ 1 FNU). For silica, the geographical pattern was similar to that of the 20 rivers in the regular monitoring: lowest concentrations on the west coast (< 1 mg/L, Suldalslågen and Ekso), medium

NIVA 7564-2020 levels in the south (1 – 2 mg/L, Mandalselva and Lygna), and higher levels when moving northeast (approximately 2 mg/L, Tovdalselva and Nidelva).

Figure 29: Average suspended particulate matter concentration (SPM, top), turbidity (middle), and silica concentration (bottom) for 2018 for the ten additional rivers included from the National monitoring program for limed rivers (dark turquoise) and the 2018 classification of ecological and chemical status (light turquoise). Error bars illustrate interannual variability (± stdev).

3.3.3 Organic carbon Based on the mean annual concentration of TOC (Figure 30), Nidelva, Tovdalselva, Mandalselva, and Lygna were all categorized as humic in 2019 (> 5 mg/L), according to the Norwegian WFD typology. This was not the case in 2018, were both Nidelva and Lygna had lower concentrations (< 5 mg/L, Gundersen et al., 2019). Ekso and Suldalslågen are in 2019 categorised as clear (2 mg/L > TOC < 5 mg /L) and very clear (< 2 mg/L), respectively. In all rivers were POC and DOC were determined, the dissolved fraction was the dominant (Figure 31), but with a significant fraction of particulate in most rivers. This contrasted with the 20 rivers in the regular programme, where the particulate fraction was negligible in most cases (except for Orreelva). The reason for this is not known, but it mirrors the pattern observed in 2018 (Gundersen et al., 2019).

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Figure 30: Average total organic carbon (TOC) concentration for 2018 for the ten additional rivers included from the National monitoring program for limed rivers (dark turquoise) and the 2018 classification of ecological and chemical status (light turquoise). Error bars illustrate interannual variability (± stdev).

Figure 31: Average 2019 distribution of particulate (POC, light turquoise)- and dissolved (DOC, dark turquoise) organic carbon (mg/L) for the six additional rivers included from the National monitoring program for limed rivers (dark turquoise). Any deviation from the TOC in Figure 29 result from analytical uncertainties in the individual methods used to determine POC and DOC.

3.3.4 Nutrients For the six rivers analysed in 2019, mean annual tot-P concentrations were relatively low (< 6 µg/L, Figure 32) and similar to last year (Gundersen et al., 2019). For tot-N there is more variation, with higher levels in the southern rivers (Nidelva, Tovdalselva, Mandalselva, and Lygna, Figure 32) compared to the rivers on the west coast (Suldalslågen and Ekso). This is likely a result of atmospheric deposition of nitrogen, from which the southern rivers have been and are most impacted.

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The high variability in tot-N concentration observed in 2018 for Nidelva should be noted. This was due to a sudden increase during July; one measurement of tot-N about ten times higher than what was measured in the other months. As discussed in last year’s report, the high tot-N value was caused by an increase in ammonium-N (2000 µg/L). Ammonium-N is typically low in Norwegian rivers, and the sudden increase could have resulted from manure application or a spill. For the remaining rivers, the distribution of the various N-fractions was in accordance with what was seen for the 20 rivers in the regular programme: dominated by nitrate-N, followed by organic-N, and with low levels of ammonium-N (Figure 33). One exception is Tovdalselva, where the ammonium-N fraction is higher.

Figure 32: Average 2018 concentration of top: total phosphorus (TOTP/tot-P) and bottom: total nitrogen (TOTN/tot-N) for the ten additional rivers included from the National monitoring program for limed rivers (dark turquoise) and the 2018 classification of chemical and biological status (light turquoise). Error bars illustrate monthly variation (± stdev).

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Figure 32: Average 2019 distribution of the following nitrogen fractions: ammonium- (dark turquoise), nitrate- (medium turquoise), and total organic- (light turquoise) nitrogen (N: µg/L) for the six additional rivers included from the National monitoring program for limed rivers.

3.3.5 Metals Analytical determination of metals was conducted by a different laboratory (Vestfold lab) than the 20 main rivers discussed earlier in this report. At Vestfold lab, the LOQs of the methods were higher than at the NIVA lab and for several of the rivers, many of the metals were not detected. For this reason, Pb, Cd, Ni, Hg, and Ag – where 48 %, 62 %, 69 % and 100 % of the measurements were < LOQ – were not included in the discussion (Table 7). Mean annual concentrations of As and Cu were below the EU WFD threshold value (0.5 µg/L and 7.8 µg/L, Table 9) for all six rivers (Table 17). Levels were similar to what was observed for most of the 20 main rivers (Figure 20 and 23).

Table 17. 2018 average metal concentrations ± stdev for additional rivers (all data in µg/L).

River As Cu Zn Cr Nidelva 0.18 ± 0.03 1.0 ± 0.4 7 ± 2 0.2 ± 0.1 Tovdalselva 0.22 ± 0.04 0.3 ± 0.4 7 ± 4 0.2 ± 0.1 Mandalselva 0.18 ± 0.04 0.7 ± 0.3 7 ± 3 0.15 ± 0.08 Lygna 0.19 ± 0.04 0.4 ± 0.2 7 ± 3 0.13 ± 0.07 Suldalslågen 0.06 ± 0.01 0.8 ± 0.9 4 ± 2 0.12 ± 0.06 Ekso 0.06 ± 0.02 0.6 ± 0.2 5 ± 3 0.10 ± 0.07

In 2018, mean annual Zn concentrations were reported to be above or close to 10 µg/L in Tovdalselva, Mandalselva, and Lygna (Gundersen et al., 2019). These concentrations were similar to levels found in Alna and Orreelva, which are heavily influenced by human activities. In 2019, concentrations were much lower in Tovdalselva, Mandalselva, and Lygna (Table 17). All Zn concentrations were below the EU WFD threshold level (11 µg/L, Table 9).

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Levels of Cr were generally low for all rivers (≤ 0.2 µg/L), much lower than the EU WFD threshold value (3.4 µg/L, Table 9).

3.4 Trends in riverine loads and concentrations

As in previous reports for the river monitoring programme, the trend analyses include an evaluation of both loads (riverine transport of dissolved and particulate matter per unit of time) and concentrations. Loads are important for assessing total transport to coastal waters, whereas concentrations will give an indication of the water quality. By evaluating trends in loads in combination with water discharge, it is possible to reveal whether potential trends are related to changes in the release of the chemical substance or in water flow.

3.4.1 Loads and concentrations of SPM, silica, TOC, and nutrients (1990-2019) Trends in water discharge (Q) were discussed in Chapter 3.1.4 but are included also here in discussion of the dataset of long-term trend analysis (1990-2019) of loads (Table 18) and concentrations (Table 19) of SPM, silica, TOC, and nutrients. Note that for TOC, the trends for certain rivers represent a shorter time period (1999 – 2019), due to limited observations during the early 1990’s. Only trends that are significant at a level of 95% (p < 0.05) will be discussed.

Spatial Patterns A geographical pattern appeared when looking at the trend analysis of loads of the various parameters (Table 18). There was a significant increase for several of the parameters for the south- eastern rivers Glomma, Drammenselva, and Numedalslågen including SiO2, PO4, and tot-N. Additionally, Drammenselva had increasing SPM, TOC, and tot-P loads, while Numedalslågen increased in tot-P and NH4. This can be seen in connection with the increased precipitation, temperature, and resulting discharge in this part of the country. For a few of the parameters (SiO2, PO4, and tot-N), the significant increase was also apparent in concentrations (Table 19). This indicates that discharge is not the only driving factor for the observed increase.

Table 18. P-values for long-term trends (1990-2019) in water discharge (Q) and loads (transport) of suspended particulate matter (SPM), silica (SiO2), total organic carbon (TOC), total phosphorus (tot-P), and phosphate (PO4), total nitrogen (Tot-N), ammonium (NH4), nitrate (NO3), in rivers.

River Q SPM SiO2 TOC* Tot-P PO4 Tot-N NH4 NO3 Glomma 0.030 0.544 0.032 0.094 0.568 0.038 0.010 0.000 0.059 Drammenselva 0.010 0.019 0.000 0.003 0.005 0.006 0.030 0.002 0.069 Numedalslågen 0.164 0.125 0.003 0.880* 0.027 0.009 0.003 0.022 0.108 Skienselva 0.116 0.721 0.007 0.786* 0.412 0.412 0.116 0.005 0.000 Otra 0.392 0.199 0.335 0.475 0.354 0.269 0.318 0.125 0.000 Orreelva 0.074 0.50 0.030 0.450 0.050 0.080 0.695 0.225 0.432 Orkla 0.521 0.669 0.943 0.412 0.669 0.568 0.830 0.000 0.803 Vefsna 0.318 0.002 0.116 0.608* 0.000 0.002 0.000 0.000 0.000 Altaelva 0.335 0.748 0.943 0.319 0.212 0.301 0.669 0.046 0.318

Red – significantly increasing p<0.05, green – significantly decreasing p<0.05

*Trend analysis started in 1999 due to limited data in the period from 1990

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Increased transport of SPM, silica, TOC, and nutrients to the coastal system can have a negative impact on the ecology in these systems (McGovern et al., 2019). Particles can influence light penetration while SiO2, OM, PO4 and nitrogen species constitute the main nutrients for diatoms and heterotrophs and can thereby affect the foodweb structure.

Among the other rivers, the agricultural influenced Orreelva located on the west-coast displayed similar increase in the loads of SPM, silica, and tot-P (Table 18) while this was not evident in the trends of concentrations (Table 19). In contrast, decreasing trends was evident for Vefsna, covering loads of SPM, tot-P, PO4, tot-N, NH4, and NO3. This picture was mirrored in the trends of concentrations, indicating that the sources of these parameters to the river had been reduced.

Table 19. Long-term trends (1990-2019) in concentrations of suspended particulate matter (SPM), silicate (SiO2), total organic carbon (TOC), total nitrogen (Tot-N), ammonium (NH4), nitrate (NO3), total phosphorus (tot-P), and phosphate (PO4) in rivers. p-values are shown.

River SPM SiO2 TOC* Tot-P PO4 Tot-N NH4 NO3

Glomma 0.040 0.107 0.612 0.725 0.000 0.943 0.000 0.391 Drammenselva 0.108 0.012 0.137 0.942 0.090 0.858 0.001 0.029 Numedalslågen 0.077 0.003 0.650* 0.162 0.031 0.038 0.678 0.762 Skienselva 0.015 0.005 0.221* 0.616 0.223 0.000 0.001 0.000 Otra 0.008 0.963 0.624 0.002 0.578 0.003 0.068 0.000 Orreelva 0.254 0.559 0.251 0.387 0.093 0.326 0.079 0.042 Orkla 0.001 0.001 0.260 0.181 0.954 0.005 0.000 0.175 Vefsna 0.001 0.117 0.693* 0.001 0.036 0.006 0.000 0.000 Altaelva 0.031 0.779 0.625 0.104 0.048 0.775 0.009 0.592

Red – significantly increasing p<0.05, green – significantly decreasing p<0.05

*Trend analysis started in 1999 due to limited data in the period from 1990

Nitrogen related changes – some examples For nitrogen, Glomma, Drammenselva and Numedalslågen all show a long-term increase in tot-N loads (Table 18). However, a concentration increase is only evident for Numedalslågen, indicating that there has been a long-term increase in water discharge for Glomma and Drammenselva. This pattern is supported by water discharge data discussed for the three rivers in chapter 3.1.4.

Skienselva shows decreasing trends of both NH4 and NO3 loads, but no change in tot-N loads (Table 18) suggesting that the organically bound nitrogen increases in Skienselva. The decrease in NH4 and NO3 is a positive development since these N species are normally quickly assimilated by plants or microbes. The reduced levels could result from increased biological activity, reduced atmospheric N- deposition, and/or, in some rivers, by increased water discharge (causing dilution). A similar pattern is evident for Vefsna.

2019 pending trends Compared to the statistical analysis undertaken last year (Gundersen et al., 2019), some changes appeared in whether trends of loads and concentrations were significant or not. For Glomma there is no longer a significant increase in the load of SiO2, Numedalslågen no longer displayed an increase in the load of NH4, while the concentration of both PO4 and tot-N was now increasing in the same

NIVA 7564-2020 river. In Orreelva, the load of tot-P is no longer increasing – likely explained by a corresponding loss of significantly increasing load of particles in the river. Moreover, NO3 concentrations are now significantly decreasing in Orreelva. Finally, in River Altaelva, NH4 loads are now significantly decreasing.

The missing TOC trends Browning of surface waters across the northern hemisphere is a well-established phenomenon, consisting of an observed increase in both the concentration and colour of DOM. For example, Evans et al. (2006) described nearly a doubling in the median TOC concentration for several streams and lakes in the United Kingdom, during the period 1988-2003. The browning has been explained by a reduction of acid deposition occurring from the mid-1970s (de Wit et al., 2007; Evans et al., 2006; Monteith et al., 2007) and/or to climate change (de Wit et al., 2016; Worrall et al., 2003). The underlying mechanism constitutes either an increased leaching of DOM from the terrestrial compartment (e.g. increased DOM solubility from reduced sulphate deposition/increased flow from increased precipitation) or increased net DOC production (i.e. from increasing temperatures).

In Norway, the largest increase in browning has been observed for lakes and streams in the south- eastern Norway (de Wit et al., 2007; de Wit et al., 2016), which is the region that has been most severely impacted by acid deposition and that contains the largest terrestrial carbon stores. While browning of lakes and streams has been frequently documented, few studies have investigated the effect in larger rivers.

Among the rivers included in this monitoring programme, only Drammenselva displayed a significant increase in TOC concentration (Table 19 and Figure 33). None of the rivers showed significant trends in the export of TOC (Table 18). A few factors can help explain the lack of expected TOC increase in these rivers:

1) Time series: the time series for five of the rivers (Numedalslågen, Skienselva, Orreelva, Vefsna, and Altaelva) are too short to capture any potential increase, starting from the year 1999. 2) Regulation of rivers: An important difference between these larger rivers and smaller lakes and streams is the hydrological conditions and the fact that most of these rivers are regulated for hydropower (see section 1.2). Of the rivers included here, Glomma, Drammenselva, Numedalslågen, Skienselva, Otra, Bjerkreimselva, Driva, Orkla, Nidelva, Vefsna, Målselva, Altaelva, and Pasvikelva are all regulated.

Point 2) is particularly important. A Swedish study found water flow (together with sulphate deposition) to be the major factor governing interannual variation in OM concentration in rivers (Erlandsson et al., 2008). This was moreover supported by a Finnish study in which interannual variation in carbon export was driven mainly by hydrological conditions (Räike et al., 2012). Lakes and streams typically have smaller interannual variation in water flow, thereby making changes in the TOC concentration over time more apparent.

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Glomma

Drammenselva

Otra

Orkla

Figure 33: Annual average TOC concentration (mg C/L) to the left, and TOC export (tonnes C), to the right, for the rivers with time series starting in or close to 1990. From top: Glomma, Drammenselva, Otra and Orkla. Sen slope is illustrated in the plots.

3.4.2 Loads and concentrations of metals (2004-2019) For metals, including Pb, Cd, Cu, Zn, and Ni, time series exist from 2004. The shorter time period was selected due to an increase in the sensitivity of the analytical methods (lower LOQ) over time, i.e. it has become possible to detect lower concentrations. As discussed in last year’s report, if data prior to 2004 were not excluded, the trend analysis could potentially have showed false decreasing trends (Gundersen et al., 2019, Skarbøvik et al., 2007, Stålnacke et al., 2009). Additionally, caution should be paid when evaluating the data as the sampling frequency has been reduced from monthly to quarterly since 2017. This can introduce uncertainty to the trend analysis, especially for polluted rivers where the seasonal variability is expected to be substantial.

Generally, loads (Table 20) and concentrations (Table 21) of metals show a significant declining trend. The only two exceptions are Vefsna and Alta, where Ni concentrations are significantly increasing (Figure 34). The reason for this is not known. However, the Ni levels in these two rivers are low and the increasing trend does not warrant major concern at this point.

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Figure 34: Concentrations of Ni in the Vefsna (left) and Alta (right). Sen slope is illustrated in the plots.

Table 20. Short-term trends (2004-2019) in metal loads in rivers monitored monthly since 1990. p-values are shown.

River Pb Cd Cu Zn Ni

Glomma 0.753 0.260 0.043 0.224 0.558 Drammenselva 0.964 0.300 0.065 0.096 0.096 Numedalslågen 0.893 0.499 0.163 0.444 0.964 Skienselva 0.558 0.192 0.115 0.893 0.053 Otra 0.620 0.392 0.008 0.008 0.034 Orreelva 0.620 0.558 0.893 0.685 0.893 Orkla 0.006 0.053 0.013 0.022 0.444 Vefsna 0.065 0.471 0.096 0.001 0.822 Altaelva 0.260 0.516 0.065 0.096 0.753

Red – significantly upward p<0.05, green – significantly downward p<0.05.

Table 21. Short-term trends (2004-2019) in metal concentrations in rivers monitored monthly since 1990. P-values are shown.

River Pb Cd Cu Zn Ni

Glomma 1.000 0.368 0.005 0.163 0.718 Drammenselva 0.241 0.007 0.000 0.001 0.071 Numedalslågen 0.620 0.715 0.043 0.022 0.498 Skienselva 0.058 0.000 0.006 0.009 0.000 Otra 0.685 0.120 0.005 0.000 0.031 Orreelva 1.000 0.961 0.240 0.964 0.085 Orkla 0.078 0.125 0.009 0.017 0.279 Vefsna 0.176 0.004 0.031 0.000 0.013 Altaelva 0.680 0.002 0.000 0.002 0.009

Red – significantly upward p<0.05, green – significantly downward p<0.05.

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A few trends, now including data until 2019, are different from last year’s report which only included data until 2018 (Gundersen et al., 2019). Skienselva do no longer have a decrease in Ni load (Table 20), Orkla no longer have a decreasing Cd load (table 20), and Vefsna have a significantly decreasing concentration of Cu (Table 21).

3.4.3 A comparison of methods for Hg flux calculations Within the main monitoring programme, samples for metals are collected four times per year and analysed for Hg using the AAS method. More frequent sampling of the same rivers (one sample per month) is also undertaken specifically for Hg and analysed using the more sensitive CVAFS method (see sections 2.2.1 and 2.2.2 for more details). Results from both methods were extracted from the database for just the dates sampled in the main programme (i.e. four samples per year for both methods). LOQ values in results from the AAS method were linearly adjusted according to the proportion of LOQ values in the dataset (as is usual for the RID/OSPAR workflow; see section 2.3). No adjustment was necessary for the CVAFS dataset, since all values were above LOQ (Table 6). Annual Hg fluxes from both methods were then estimated using the standard OSPAR methodology.

Fluxes determined with the two different methods were in good agreement (Figure 35 and 36). Generally, fluxes are similar for the two methods. Nonetheless, the AAS method underestimates the loads, compared to CVAFS in some cases: for rivers Pasvikelva (13.5 kg Hg with CVAFS method, 5.0 kg Hg with AAS method) and Tanaelva (12.7 kg and 0 kg respectively), while their overestimates the loads in other cases: for rivers Drammenselva (10.7 kg and 32.0 kg respectively) and Skienselva (9.5 kg and 14.6 kg respectively).

Figure 35: Fluxes of Hg in 16 rivers calculated based on analytical results from the CVAFS method (y- axis) and the AAS method (x-axis). The black line indicates the 1:1 ratio. Note that were no data available for Bjerkrheimselva, Vikedalselva, Nidelva, and Målselva (Figure 36) based on the AAS method due to all measurements being < LOD.

Based on the CVAFS method, the highest loads of Hg in 2019 were found in Glomma (46.4 kg), nearly four times as much as in the other rivers: 13.5 kg in Pasvikelva, 12.7 kg in Tanaelva, 10.7 kg in Drammenselva, and 9.5 kg in Skienselva. This is the first time that loads are calculated and reported for Hg in this cycle of the river monitoring programme.

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Figure 36: Fluxes of Hg in the main 20 rivers calculated based on analytical results from the CVAFS method (blue bars) and the AAS method (red bars). Note that were no data available for Bjerkrheimselva, Vikedalselva, Nidelva, and Målselva based on the AAS method due to all measurements being < LOD.

3.5 Quality of dissolved organic matter Dissolved organic matter (DOM) is operationally defined as the fraction of organic carbon that can pass through a filter with 0.45 µm pore size. DOM is considered the most bioavailable and reactive fraction of the OM. The quality of DOM is governed by its source material, hydrological and climatic conditions, in addition to various local transformation processes (Harms et al., 2016; Mutschlecner et al., 2018; Shatilla and Carey, 2019). Light absorption at a certain wavelength can be attributed to specific molecular segments or functional groups in the DOM, and hence several spectral indices have been defined to describe characteristics such as the degree of aromaticity (sUVa) and molecular size (E2_E3) (Peuravuori and Pihlaja, 1997; Weishaar et al., 2003) (Table 22).

Table 22. Overview of the absorbance indices used to describe DOM quality Name Definition Characteristic sUVa Specific UV (Abs Aromaticity (positive relationship) absorbance 254nm / DOC2)*100 E2_E3 250 nm Aromaticity (negative relationship) /365nm Molecular size (negative relationship)

For this analysis, seasonal and regional patterns in DOM quantity (TOC) and quality (sUVa and E2_E3) have been investigated, in accordance with the two preceding years. The rivers have been grouped geographically according to the four major drainage basins in Norway (Barents Sea, Norwegian Sea, North Sea, and Skagerrak). The results for the main rivers will be discussed separately from the additional rivers since the analyses have been conducted by two different laboratories (NIVA lab and Vestfold lab).

3.5.1 Main Rivers Seasonal variation For Norway as a whole, the mean seasonal TOC concentration showed a steady increase from winter through summer, a drop in august followed by a continued increase during autumn (Figure 36). This reflects the typical seasonal events of increased production and transport of OM to the river during

2 TOC has been used instead of the DOC (< 0.45 µm) due to more extensive data availability.

NIVA 7564-2020 spring warming with snow melt. The drop in TOC concentration in august was likely caused by a combination of high biological uptake and low precipitation that reduced transport of new OM. During autumn, intensive rainfall ensured increased transport of TOC from the forest floors to the rivers. Relatively large differences were apparent in the regional averages, likely reflecting differences in climatic and hydrological conditions (Harms et al., 2016; Mutschlecner et al., 2018). For example, in spring, a peak in TOC concentration can typically be associated with increased transport to the river with snow melt. While this peak appeared already in April for the rivers draining to Skagerrak, the effect from snow melt was not evident in the rivers draining to the Barents Sea until June.

Figure 36: Monthly average values of TOC concentration, aromaticity (sUVa), and molecular size (E2_E3) for rivers in the four regions Barents Sea (red, n = 4), North Sea (green, n = 5), Norwegian Sea (blue, n = 4), and Skagerrak (purple, n = 7). The black dashed line shows monthly averages for all rivers (n = 20).

Interestingly, the regional averages appeared more uniform when looking at sUVa, except for the Barents Sea (Figure 36). During the summer months, the sUVa was overall higher than the rest of the year reflecting higher aromaticity before dropping in august at the same time as the TOC concentration dropped. This may be due to the production of new OM during summer, displaying a wide range in DOM qualitative parameters before transformation processes such as photodegradation.

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The E2_E3 index shows contrasted seasonal variations in the south and in the north reflecting changes in molecular size (Figure 36). For the rivers draining to the Barents Sea and Norwegian sea the molecular size increased during summer, while it was the opposite for rivers draining to Skagerrak and the North Sea. The DOM in the rivers draining to the Barents Sea had the lowest molecular size at the beginning of the year and showed the largest relative increase during summer. The reason for these findings in DOM molecular size is not known.

A comparison of TOC concentrations, sUVa, and E2_E3 for the last three years revealed that the inter-river variation is the largest source of variability (Figure 37). However, the summer months in 2018 (June-Aug) display a distinct pattern with low TOC, low sUVa and high E2_E3 which could have been related to the unnormal warm and dry summer as hypothesized by Gundersen et al. (2019). Interestingly, August 2019, which was also drier and warmer than normal, show similar characteristics.

Figure 37: Monthly averages values of TOC concentration, aromaticity (sUVa), and molecular size (E2_E3) for 2017 (orange), 2018 (blue), and 2019 (green) for all 20 rivers in the monitoring programme. Error bars illustrate the variability in the data (± stdev).

Regional variation Using monthly averages, the relationships between i) TOC concentration and water discharge, ii) DOM aromaticity (sUVa) and TOC concentration, and iii) DOM molecular size (E2_E3) and TOC concentration are explored (Figure 38). Note that Alna and Orreelva have been excluded from this analysis for being atypical of their regions (high particle load from human influence). Regional patterns in the quantity and quality of DOM were similar to the findings from 2018 (Gundersen et al., 2019). The DOM in the rivers draining to the North Sea (green) was most different from the other regions, being low in TOC despite a high water discharge, and relatively high in DOM aromaticity (sUVa) and molecular size (E2_E3). The Skagerrak region rivers were highest in TOC with a moderate water discharge, and the DOM was of comparable high aromaticity and molecular size to the rivers draining to the North Sea. The rivers draining to the Barents Sea were characterized by intermediate TOC levels and low water discharge, and the DOM was of the lowest aromaticity and molecular size. The Norwegian Sea rivers were intermediate for all parameters. These regional patterns are likely a

NIVA 7564-2020 reflection of differences in climate, hydrology, and other terrestrial conditions. To be able to explain these findings in more detail, the data should be analysed in combination with data on land use, climate, and other catchment characteristics.

Figure 38: Relation between monthly TOC concentration and discharge (top left), aromaticity (sUVA) and TOC concentration (top right), and molecular size (E2_E3) and TOC concentration (bottom left) for rivers draining to the Barents Sea (red, n = 4), the North Sea (green, n = 4), the Norwegian Sea (blue, n = 4), and Skagerrak (purple, n = 6). Note that Rivers Alna (Skagerrak) and Orreelva (North Sea) have been excluded from the figures.

3.5.2 Additional Rivers The six additional rivers included from the National monitoring program for limed rivers are divided into their representative drainage regions of Skagerrak (n=3) and the North Sea (n=3) for the evaluation of TOC concentrations, DOM aromaticity (sUVa) and molecular size (E2_E3) (Figure 39).

Seasonally, the rivers draining into both seas showed two peaks in TOC concentrations that corresponded to spring snow melt and autumn intensive rain. For the south-eastern region of Skagerrak, the onset of the spring peak was earlier than for the western North Sea region. The

NIVA 7564-2020 aromaticity of the DOM (sUVa) in the rivers from both regions were at its lowest during the spring and summer months. This contrasted with the findings from the main rivers of the monitoring programme (Figure 37). The reason for this is not known, but it may reflect local differences in climate and/or catchment characteristics. The DOM molecular size in the rivers draining to the North Sea appeared to have been very low during the beginning of the year. However, the high values are partly believed to result from high uncertainty in the data due to the low TOC concentrations.

The regional relationships between TOC concentration and DOM aromaticity, and between TOC concentration and DOM molecular size reported for these additional rivers appeared to be similar to those of the main monitoring programme. TOC concentrations in the North Sea rivers was lower than in the Skagerrak region rivers , but comparable DOM aromaticity and molecular size were reported in rivers from both regions.

Figure 39: For the additional rivers draining to the regions of the North Sea (green, n = 3) and Skagerrak (purple, n=3), left panel: Monthly regional averages of TOC concentration, aromaticity (sUVa), and molecular size (E2_E3), and right panel: relation between monthly averages of aromaticity (sUVa) and TOC concentration, and molecular size (E2_E3) and TOC concentration.

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3.6 Modelling of contaminants in the Alna river

3.6.1 Calibration results

Calibration of the hydrological model The hydrological model PERSiST was calibrated on a time interval from 2006 to mid-2015. Later dates were excluded from the calibration due to an irregular flooding event in September 2015, when there was high uncertainty in the flow measurements as the river had a change in its regulation pattern. The hydrological model was calibrated against this discharge data using a combination of manual and automatic calibration with Python’s LMFIT package. The calibration achieved a Nash-Sutcliffe coefficient of 0.53, which is a relatively good fit when considering that Alna is largely an urban catchment with complex subsurface drainage systems that are hard to simulate with a catchment model. Results of the calibration are displayed in Figure 40.

Calibration of INCA-Sed and INCA-Tox-C INCA-Sed was calibrated against suspended particulate matter data collected in the Alna river by the River Monitoring Programme from 2012 until 2018. It was manually calibrated because the autocalibration had trouble to obtain stable levels of riverbed sediments with no trends over time. The calibration obtained a Nash-Sutcliffe coefficient of 0.24, and the results are displayed in Figure 41.

INCA-Tox-C was calibrated so that simulated DOC levels in the river matched measured DOC data from the River Monitoring Programme as close as possible. Since the DOC data was patchy, we also used TOC data (measured TOC and DOC were similar at dates when both were available). It is not critical to get an exact match of the model to the data since the contaminant module only relies on the general level being correct (Figure 42).

The soil assumed to contain about 20 kg of soil organic carbon (SOC) per m2. This corresponds to a SOC content of about 4%. The final contaminant results were not too sensitive to this value unless it was changed by more than an order of magnitude, and so it is likely to be reliable. It was assumed that the proportion of SOC that engages in fast exchange with the soil water and air is 5% of the total, while the rest is a more slow-responding buffer.

Calibration of the main contaminant module in INCA-Tox Data for contaminant concentrations in the river comprised both the dissolved and particulate phase. Dissolved contaminants were collected by passive samplers that were deployed in the river for about three months. Hence, each measurement corresponds to an average for the actual time span.

The selected contaminants all have high octanol-water partitioning coefficients, which mean that they are highly hydrophobic and have a large affinity to organic matter. Hence, most of the contaminants in the soil will be associated with SOC. This means that the modelled concentration of contaminants in SOC will govern the dissolved concentration in soil water, which again is the main driver for the river dissolved concentrations. The hydrologic residence time for Alna river is too short for the exchange between the river and atmosphere to have a large impact. This means that contaminant concentrations in the river are sensitive to contaminant concentrations in the SOC, highlighting the importance to work with realistic contaminant concentrations in the SOC.

By choosing initial concentrations in SOC within the ranges given by data from forested areas around Oslo (Table 12), we obtained some river dissolved contaminant concentrations that matched the

NIVA 7564-2020 general magnitude of observed data. However, the computed deposition of contaminants was not sufficient to sustain the level in the soil, even when dry deposition velocities were assumed to be the maximal theoretical value. We thus had to add in a superficial deposition of contaminants that was 10-100 times larger than the computed deposition from NILU’s station at Birkenes (in Agder county) in order to sustain the levels in the soil. Dedicated measurements of atmospheric deposition from the Oslo urban area would have substantially constrained this uncertainty.

Monitoring of PCBs and PAHs concentrations in Norway is routinely performed at remote stations. In this case, the closest station was Birkenes in the extreme south of Norway. We would expect atmospheric concentrations of PCBs and PAHs in proximity to a large city like Oslo to be considerably larger than at a more remote station like Birkenes. Previous studies have shown PCB and PAH levels being 10-100 times higher in proximity of cities compared to rural sites (Liu et al., 2014; Menichini et al., 2007). In support to this explanation, we compared data of Benzo-a-Pyrene monitored in Oslo (through a programme run by Oslo municipality) with the levels at Birkenes, and the urban levels were typically 10-20 times higher.

Calibrating contaminant concentrations in suspended particulate matter turned out to be difficult because the measured concentrations are about 1000 times lower than what they would be if they were in equilibrium with the aqueous concentrations (based on the water-SOC partitioning coefficient, and assuming a carbon content of the suspended matter of 1%). Given that particle- bound contaminants account for less than 1% of the total contaminant transport in the river, getting these numbers right does not have a significant impact on the total fluxes.

The calibration results for PCB-101, PCB-153, Phenanthrene, Fluoranthene, and Benzo-a-Pyrene are showed in Figure 43 to Figure 47 on the next pages. The figures show that simulated concentrations were in the same range as the observed values.

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Figure 40. Modelled vs. observed river flow

Figure 41. Modelled vs. observed suspended solids (SS)

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Figure 42. Modelled vs. observed dissolved organic carbon (DOC).

Figure 43. Modelled vs. observed PCB-101. Note that data displayed in 2011 are not from different points in time, but from 4 different locations in the river.

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Figure 44. Modelled vs. observed PCB-153. Note that data displayed in 2011 are not from different points in time, but from 4 different locations in the river.

Figure 45. Modelled vs. observed Phenanthrene. Note that data displayed in 2011 are not from different points in time, but from 4 different locations in the river.

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Figure 46. Modelled vs. observed Fluoranthene. Note that data displayed in 2011 are not from different points in time, but from 4 different locations in the river.

Figure 47. Modelled vs. observed Benzo-a-Pyrene. Note that data displayed in 2011 are not from different points in time, but from 4 different locations in the river.

3.6.2 Discussion of climate drivers affecting contaminant transport

Model sensitivity to climate-dependent biogeochemical parameters An earlier application of the original INCA-Contaminant model provides useful insights on the expected influence of climate on mobilization of hydrophobic contaminants in boreal catchments (Nizzetto et al., 2016). The key premise to introduce this analysis is that climate drivers (i.e. mainly changes in mean temperature and precipitation patterns) not only have a direct effect on temperature-controlled contaminant partitioning and on the water-driven contaminant runoff, but it also affects turnover of organic matter in soils. This organic matter is the main pool of PCBs and PAHs in the catchment, and even a small change in the size of this capacitor may result in substantial changes in mobilisation and fluxes of contaminants in the catchment.

Nizzetto et al. (2016) analysed model output sensitivity to a number of hydro-biogeochemical parameters at Sandvikselva (Bærum municipality) which has similar characteristics as Alna. Model outputs were particularly sensitive to climate-dependent biogeochemical parameters. Rate coefficients for SOC production and consumption were very influential parameters with the mineralization rate of SOC being the most sensitive. Also, the rate of DOC production from SOC also ranked among the most influential parameters. Hydrologic time constants and temperature-related parameters all influenced model performance significantly. As expected, model outputs were also highly sensitive to varying Kow value for compounds with LogKow > 6.2, suggesting that climate influences on contaminant outflow will be more pronounced for more hydrophobic substances such as PCB 153 or benzo-a-pyrene. The results highlight the need for credible hydro-chemical simulations when modelling the fate and transport of environmental contaminants that may be bound to SOC and suspended sediments.

Simulation of future climate influence on contaminant fate Climate influence on the long-term fate of PCB101 has previously been evaluated using INCA- Contaminants on a boreal forest sub-catchment (70 km2) in Sandvikselva, close to Oslo and with largely the same contaminant exposure as the Alna river (Nizzetto et al., 2016). As the soil OM hosts most of the PCBs and PAHs in the catchment, and considering the sensitivities elucidated in the previous section, the assessment of climate controls over contaminant fate and transport is performed considering the forested part of the catchment. Contaminants inputs in the urban part of the catchment will be instead prevalently sensitive to anthropogenic drivers, such as changes in technology, chemical management and pollution control. Simulations were carried out for the period 2006-2050 considering two climate scenarios: Scenario 1 “as-today” which considers future climate consistent with present conditions and Scenario 2 “RCP8.5” which projects a mean temperature increase of 2.5°C and total rainfall increasing with 20% by 2050 (IPCC 2013).

The simulation suggests that this typical boreal forested catchment is currently in a transitory phase in which yearly integrated re-emission of PCB to the atmosphere and to surface waters will exceed the total atmospheric inputs during the next decade. In other words, the system will shift from acting as a net sink of atmospheric PCBs to becoming a net source for air and water environments. Riverine discharge is the major contribution to the total physical output of contaminants, representing over 95% of the total PCB 101 re-emission from the catchment during the current decade. This output is predicted to decline by one order of magnitude by 2050 while net volatilization will still be in the same range as that calculated for the present time period. Climate change is projected to have very little influence on this trend which is

largely governed by the steady decline in atmospheric concentrations of PCBs observed during the last two decades in boreal environments (Schuster et al., 2010).

These results support the rising importance of environmental reservoirs such as boreal soils as secondary atmospheric sources of POPs in a regime with declining atmospheric concentrations and are central for understanding future distribution of POPs and effectiveness of international regulation on these contaminants (Nizzetto et al., 2010).

3.7 High-frequency monitoring in Rivers Storelva and Målselva

Rivers Storelva and Målselva are selected for more detailed studies on the effects of climate variability and climate change on rivers. To study short-term effects of climate variability on water chemistry, sensors that measure water temperature, pH, conductivity, turbidity and FDOM are deployed in both rivers. The sensor stations are located at the same location as the manual sampling stations, and the data is collected on an hourly basis.

3.7.1 River Storelva

Water flow The flow dynamics in River Storelva are characterized by rapid responses to precipitation events with a relatively quick return to the baseline level after the flood peak. There is no distinct seasonal pattern, and flood events can occur in all seasons, also during winter. In 2019, there was an early snowmelt flood lasting from the second week of February until the end of March (Figure 48). This is supported by data from the met.no station Nelaug (lat: 58.6582, lon: 8.63) where snow started to accumulate in late January and reached a maximum of 100 cm in mid-February, whereafter it largely melted away during the last part of February. There were smaller rainwater floods before and after summer, and much shorter period with summer low- flow than in 2019. From late September there were four distinct rainfall floods, with the largest reaching 115 m3/s in late November.

Water temperature The water temperature in 2019 exceeded 10oC on April 20th, which was nearly 3 weeks earlier than in 2018 (Figure 48). On the other hand, the period with water temperatures above 20oC was much shorter than in 2018, lasting from late June until early August. The temperature fell below 10oC on October 8th, which was nearly three weeks earlier than in 2017 and 2018.

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Figure 48. Water temperature and water flow at the outlet of River Storelva in 2019. The water flow data are from NVE’s station 18.4.0. Lundevann.

pH River Storelva has been heavily affected by acidification due to long-range transported air pollution and since the 1990s the river has been limed to protect the salmon and sea trout populations from toxic waters. The target pH value for the liming varies throughout the year and is highest (6.4) during the smolt migration period in the spring (usually set to the period from April 1st to June 15th). In other parts of the year the pH should be kept above 6.0. The continuous pH monitoring in 2019 shows that the pH was kept above 6.0 most of the time and that there was relatively good accordance between the sensor data and pH measured in grab samples (Figure 49). Since the lime addition is automatically regulated by water flow and pH downstream the lime dozer, pH values in the river rarely drop significantly during flood events. However, smaller pH drops were measured during the flood peak in early March (pH 6.0) and in late September (pH 6.2).

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5.5 0 Jan/2019 Feb/2019 Mar/2019 Apr/2019 May/2019 Jun/2019 Jul/2019 Aug/2019 Sep/2019 Oct/2019 Nov/2019 Dec/2019 Figure 49. pH and water flow at the outlet of River Storelva in 2019. The water flow data are from NVE.

Conductivity The conductivity, which is a measure of the ionic concentration in water, was relatively stable throughout the year with values around 3-6 mS/m (Figure 50). The conductivity shows different responses to flood dynamics during the year. At the very start of the snowmelt flood early February, the conductivity peaked to the year’s highest level at 8 mS/m. This phenomenon is seen in other studies with high-frequency monitoring and is explained by elution of ions from the seasonal snowpack and sub-surface lateral flow in an early stage of the melting process. As the snowmelt proceeds the runoff is diluted by low-ionic water from the remaining snowpack. During most rainfall floods later in the year, conductivity peaked right before or simultaneously with the flood peak. One exception was the large flood in November, which led to ionic dilution and falling conductivity.

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Turbidity Turbidity is related to suspended particulate matter that affect the clarity of water. In River Storelva, with clay soils in the lower parts of the catchment, the turbidity increases quickly during flood episodes (Figure 51). In 2019, this was especially the case in May, June and in the early autumn. Later in the autumn, floods did not seem to mobilise particles to such an extent and in some cases (as in mid-October) particle concentrations decreased during a flood event. Also, during the snowmelt flood in February and March turbidity in the river was relatively low, suggesting that soil particles are largely protected from erosion under the melting snow. As in 2018, the turbidity was relatively high during the low-flow period in summer. It can be related to increased concentrations of phytoplankton in the upstream Lake Lundevann, or possibly resuspension of bottom sediments close to the sensor station at low lake water levels. The sensor data were in most instances higher than turbidity values measured in grab samples. One reason is that monthly grab samples tend to miss flood events and usually underestimate the total particle load throughout the year.

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Figure 51. Turbidity and water flow at the outlet of Storelva in 2019. The water flow data are from NVE. fDOM Fluorescent dissolved organic matter (the fraction of CDOM that fluoresces) can be used as proxy for dissolved organic carbon (DOC) in water. As can be seen in Figure 52, DOC measured in grab samples largely follows the seasonal pattern emerging from the high-frequency data. In general, fDOM in River Storelva follow a seasonal pattern with the highest concentrations during the autumn and winter period and declining concentrations throughout the summer period. The largest fDOM peaks in 2019 were recorded during a small flood in June and during the first flood after summer. The post-summer fDOM peak was almost identical to the pattern in 2018, when concentrations suddenly increased from 30-40 to 70-80 quinine sulphate units (QSU) as a response to the increased water flow.

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3.7.2 River Målselva

Water flow The flow pattern in River Målselva is dominated by strong seasonal signals (Figure 53). During winter, practically all precipitation accumulates as snow and the small water discharges are mainly supplied by groundwater. The highest water discharges are usually associated with snowmelt, first in the lower parts of the catchment and later in the upper, mountainous parts. In 2019, there was a small flood in February, whereas the major snowmelt flood lasted from late April until the maximum flow level (nearly 500 m3/s) was reached in the beginning of July. After that time the water flow decreased gradually, without any significant flood peaks, during rest of the year.

Water temperature The water temperature was around zero during all the wither months from late November to end of April (Figure 53). The water temperature crossed the 10oC limit on July 8th and reached a maximum of 16oC on July 28th. After that, the temperature fell below 10oC on September 6th.

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0 0 Jan/2019 Feb/2019 Mar/2019 Apr/2019 May/2019 Jun/2019 Jul/2019 Aug/2019 Sep/2019 Oct/2019 Nov/2019 Dec/2019 Figure 53. Water temperature and water flow at the outlet of River Målselva in 2019. The water flow data are from NVE’s station 196.35.0 Målselvfossen. pH River Målselva is well-buffered and little affected by long-range transported air pollution. The pH-values are relatively stable around 7.5 with small seasonal variations (Figure 54). River ice can cause challenges with maintaining the calibration routines during the cold season, and in 2019 this led to large gaps in the time series. As can be seen from the manual samples, the pH values were relatively stable around 7.5, except at one occasion in August when pH was 7.1.

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6.0 0 Jan/2019 Feb/2019 Mar/2019 Apr/2019 May/2019 Jun/2019 Jul/2019 Aug/2019 Sep/2019 Oct/2019 Nov/2019 Dec/2019 Figure 54. pH and water flow at the outlet of River Målselva in 2019. The water flow data are from NVE.

Conductivity The conductivity in River Målselva usually shows a weak seasonal signal with the lowest values (around 4-5 mS/m) during winter and spring (Figure 55). In 2019, values during spring were a bit higher than in 2018, possible due to somewhat higher water flow in late winter/early spring compared to the previous year. During the snowmelt period, peaks in water flow was accompanied by drops in conductivity due to dilution by meltwater with low ionic strength. Ionic dilution is less common during rainfall floods when high-flow events can promote erosion and increased solute concentrations in water. There was generally good accordance between the sensor data and conductivity measured in grab samples.

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0 0 Jan/2019 Feb/2019 Mar/2019 Apr/2019 May/2019 Jun/2019 Jul/2019 Aug/2019 Sep/2019 Oct/2019 Nov/2019 Dec/2019 Figure 55. Conductivity and water flow at the outlet of River Målselva in 2019. The water flow data are from NVE.

Turbidity As the lower parts of the river are relatively flat with several meander bends, sediment is easily resuspended during high-flow events. Flood peaks are therefore usually accompanied by

significant turbidity peaks in this river. In 2019, turbidity values showed a strong response to flood peaks in the high-flow period in May and June (Figure 56). Values were especially high (<300 NTU) during the major flood peak that occurred in early July. None of the turbidity peaks were captured by the manual sampling, which demonstrates that monthly sampling often misses short-term episodes in rivers. The remaining parts of the year were characterised by low turbidity, mostly values below 5 NTU.

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0 0 Jan/2019 Feb/2019 Mar/2019 Apr/2019 May/2019 Jun/2019 Jul/2019 Aug/2019 Sep/2019 Oct/2019 Nov/2019 Dec/2019 Figure 56. Turbidity and water flow at the outlet of Målselva in 2019. The water flow data are from NVE. fDOM The fluorescent dissolved organic matter (fDOM) signal is also closely connected to water flow in River Målselva (Figure 57). Even the small flood in late February resulted in a distinct fDOM peak. The same pattern occurred during the floods in late April and May, but interestingly, the largest flood peak in early July was not accompanied by a corresponding peak in fDOM. This might be explained by snowmelt and delivery of low-DOC water from the upper, mountainous parts of the catchment.

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4. Conclusion

Monitoring for various chemical, physical, and hydrological parameters in 20 rivers distributed along the Norwegian coastline was undertaken in 2019 as part of the Norwegian River Monitoring Programme. 2019 was a relatively warm and wet year, with air temperatures 1.2 oC above the 1961-1990 normal and 15 % more precipitation.

The monitored river catchments displayed water chemistry patterns in relation to their respective variation in land-use, elevation, vegetation and soils types. Typically, acidic rivers are found in the south and south-west, weakly acidic to neutral in the south-east, and close to neutral in mid-to-northern Norway. Bjerkreimselva, Vikedalselva, Vosso, Vefsna, Driva, and Målselva have very clear water (TOC < 2mg/L) according to the Norwegian WFD typology; Glomma, Alna, Drammenselva, Numedalslågen, Skienselva, Otra, Nausta, Orkla, Nidelva, Altaelva, Tana and Pasvikelva are clear (2 mg/L < TOC < 5 mg/L); while Storelva and Orreelva are humic (TOC > 5 mg/L). For nutrients, concentrations of tot-P and tot-N are generally below the good/moderate boundary.

There are two clear exceptions from the general water chemistry patterns among the 20 investigated rivers – Alna and Orreelva – both heavily influenced by human activities through urbanization and agriculture, respectively. These rivers typically have higher pH values and Orreelva also elevated TOC concentrations, likely due to effluent inputs and diffuse water discharge from agriculture. Alna and Orreelva are also high in turbidity, SPM and they display less than good ecological status according to the good/moderate boundary for tot-P and tot-N for their respective waters. Alna also has high metals concentrations, including As, Pb, Cd, Cu, Zn, Cr, Ni, and Hg. But mean annual 2019 concentrations were lower than the five-year (2014- 2018) mean for all metals where historical data exists.

Pasvikelva also shows elevated concentrations of selected metals, most noteworthy Ni and Cu, likely due to metallurgical activity on the Russian side of the border. However, for metals, most rivers show significantly declining trends in loads and concentrations. The only two exceptions are Vefsna and Alta, where Ni concentrations are significantly increasing. However, concentrations are low and does not warrant major concern at this point.

The long-term monitoring of river water in Norway does not display a significant increase in export of TOC. Browning of surface waters across the northern hemisphere is a well- established phenomenon, consisting of an observed increase in both the concentration and colour of DOM. The lack of expected TOC increase in the large Norwegian rivers are likely explained by the fact that time series are too short, and that rivers are regulated for hydropower.

Simulations of the fate and transport of PCB and PAH in Alna using the INCA-Tox model provided dissolved contaminant concentrations that matched the general magnitude of data measured during the period 2013-2016. Boreal forested catchments are currently in a transitory phase, where the system will shift from acting as a net sink of atmospheric PCBs to becoming a net source for air and water environments. High-frequency (hourly) sensor data from Storelva and Målselva, including water temperature, pH, conductivity, turbidity and fDOM gave new insights into how flood characteristics (i.e. type, magnitude, timing) influenced short-time variation in concentrations of dissolved ions (conductivity), suspended particles (turbidity) and DOM in 2019.

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6. Appendix A

6.1 Riverine concentrations in 2019

Glomma ved Sarpsfoss Date Qs pH KOND TURB860 SPM TOC DOC Part. C PO4-P TOTP TDP NO3-N NH4-N TOTN Tot. Part. SiO2 Ag As Pb Cd Cu Zn Ni Cr Hg N DD.MM.YYYY [m3/s] [] [mS/m] [FNU] [mg/l] [mgC/l] [mgC/l] [µgC/l] [µgP/l] [µgP/l] [µgP/l] [µgN/l] [µgN/l] [µgN/l] [µgN/l] [mgSiO2/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [ng/l] 07.01.2019 442.25 7.11 4.89 1.30 1.21 3.70 3.70 152.00 2.00 6.00 4.00 420.00 33.00 500.00 14.20 3.75 05.02.2019 573.10 7.20 4.92 1.00 0.93 2.40 2.40 140.00 2.00 5.00 2.00 340.00 31.00 470.00 10.90 3.56 0.00 0.11 0.09 0.01 1.09 13.80 0.55 0.12 4.00 05.03.2019 511.98 7.02 7.21 24.00 21.50 4.10 4.10 625.00 22.00 36.00 9.00 1100.00 <2.0 1600.00 68.60 4.63 01.04.2019 527.63 7.07 5.74 6.20 5.23 4.20 4.30 137.00 7.00 10.00 6.00 670.00 <2.0 830.00 <1.0 4.05 06.05.2019 1384.92 7.03 4.06 7.50 11.60 5.80 5.40 515.00 9.00 17.00 6.00 280.00 10.00 500.00 44.10 4.07 0.01 0.17 0.33 0.02 2.26 6.60 0.85 0.27 3.00 14.05.2019 1251.63 6.87 4.07 3.00 5.38 4.10 4.10 332.00 7.00 12.00 4.00 220.00 <2.0 410.00 27.90 3.26 20.05.2019 1208.82 6.95 3.91 3.70 5.03 5.30 5.20 297.00 9.00 11.00 3.00 260.00 20.00 520.00 <1.0 3.41 03.06.2019 1506.51 7.05 4.00 3.10 7.33 3.70 3.70 312.00 7.00 14.00 3.00 290.00 13.00 450.00 36.10 3.15 17.06.2019 1976.86 7.24 4.83 15.00 14.00 4.60 4.70 445.00 2.00 62.00 5.00 500.00 <2.0 770.00 79.80 4.07 24.06.2019 1564.87 7.26 4.46 6.50 7.40 4.40 4.30 337.00 8.00 15.00 3.00 350.00 4.00 510.00 38.00 3.64 08.07.2019 703.66 7.36 4.58 2.50 3.65 3.50 3.50 302.00 5.00 10.00 5.00 270.00 17.00 440.00 44.30 2.81 05.08.2019 519.01 7.45 5.02 1.70 2.88 2.80 2.70 286.00 4.00 8.00 2.00 240.00 7.00 390.00 30.80 2.70 0.00 0.16 0.12 0.01 1.23 1.50 0.53 0.13 <1.0 02.09.2019 877.79 7.13 5.18 16.00 17.30 4.80 4.80 454.00 16.00 30.00 10.00 490.00 11.00 700.00 62.10 2.81 07.10.2019 799.60 7.09 5.02 6.40 7.40 5.00 5.00 341.00 7.00 14.00 5.00 390.00 2.00 620.00 35.80 3.43 <0.002 0.17 0.24 0.01 1.46 3.30 0.89 0.33 <1.0 06.11.2019 634.57 7.08 4.92 10.00 8.66 6.80 6.90 338.00 9.00 19.00 7.00 410.00 <2.0 630.00 36.20 4.16 02.12.2019 649.63 6.92 5.30 17.00 12.80 7.80 7.30 487.00 15.00 26.00 10.00 550.00 4.00 770.00 21.20 5.46 Lower avg. 945.80 7.11 4.88 7.81 8.27 4.56 4.51 343.75 8.19 18.44 5.25 423.75 9.50 631.88 34.38 3.68 0.00 0.15 0.19 0.01 1.51 6.30 0.70 0.21 1.75 Upper avg.. 945.80 7.11 4.88 7.81 8.27 4.56 4.51 343.75 8.19 18.44 5.25 423.75 10.12 631.88 34.50 3.68 0.01 0.15 0.19 0.01 1.51 6.30 0.70 0.21 2.25 Minimum 442.25 6.87 3.91 1.00 0.93 2.40 2.40 137.00 2.00 5.00 2.00 220.00 2.00 390.00 1.00 2.70 0.00 0.11 0.09 0.01 1.09 1.50 0.53 0.12 1.00 Maximum 1976.86 7.45 7.21 24.00 21.50 7.80 7.30 625.00 22.00 62.00 10.00 1100.00 33.00 1600.00 79.80 5.46 0.01 0.17 0.33 0.02 2.26 13.80 0.89 0.33 4.00 More than 70% >LOD yes yes yes yes yes yes yes yes yes yes yes yes no yes yes yes yes yes yes yes yes yes yes yes no n 16.00 16.00 16.00 16.00 16.00 16.00 16.00 16.00 16.00 16.00 16.00 16.00 16.00 16.00 16.00 16.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 St.dev 470.68 0.16 0.81 6.80 5.81 1.39 1.31 136.84 5.47 14.45 2.62 219.42 10.31 292.74 22.56 0.72 0.01 0.03 0.11 0.01 0.52 5.43 0.19 0.10 1.50

Alna Date Qs pH KOND TURB860 SPM TOC DOC Part. C PO4-P TOTP TDP NO3-N NH4-N TOTN Tot. Part. N SiO2 Ag As Pb Cd Cu Zn Ni Cr Hg DD.MM.YYYY [m3/s] [] [mS/m] [FNU] [mg/l] [mgC/l] [mgC/l] [µgC/l] [µgP/l] [µgP/l] [µgP/l] [µgN/l] [µgN/l] [µgN/l] [µgN/l] [mgSiO2/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [ng/l] 07.01.2019 0.49 7.78 48.60 2.30 2.68 3.00 3.00 381.00 58.00 67.00 51.00 1150.00 460.00 1700.00 43.40 6.79 05.02.2019 0.39 7.88 51.60 2.00 2.09 3.00 2.80 405.00 48.00 65.00 41.00 820.00 230.00 1700.00 36.50 6.56 0.02 0.26 0.17 0.02 1.90 6.50 0.59 0.15 <1.0 05.03.2019 2.54 7.84 80.00 6.70 9.94 3.70 3.60 556.00 32.00 35.00 17.00 1200.00 6.00 2000.00 50.20 7.50 02.04.2019 2.02 7.86 41.60 5.50 7.53 3.80 3.80 790.00 24.00 34.00 12.00 990.00 71.00 1300.00 43.50 6.47 09.05.2019 0.75 7.93 45.90 2.70 4.21 2.40 2.10 423.00 36.00 54.00 25.00 780.00 95.00 1300.00 <1.0 4.46 0.01 0.31 0.32 0.03 2.65 8.50 0.68 0.19 <1.0 04.06.2019 0.99 7.93 46.90 2.40 3.70 2.60 2.60 456.00 28.00 46.00 26.00 800.00 35.00 1100.00 47.50 5.72 03.07.2019 0.53 8.11 48.40 5.60 5.63 2.70 2.50 452.00 45.00 59.00 34.00 1130.00 59.00 1600.00 25.60 6.71 06.08.2019 0.46 8.09 47.60 3.20 2.85 3.20 3.10 334.00 77.00 93.00 58.00 1150.00 <2.0 1700.00 22.30 6.56 0.01 0.52 0.68 0.03 2.91 14.60 1.48 0.24 <1.0 05.09.2019 52.59 7.75 27.10 61.00 104.00 11.10 8.50 2114.00 140.00 170.00 36.00 780.00 <2.0 1300.00 190.00 5.87 01.10.2019 2.30 7.86 32.00 5.80 10.10 5.50 5.30 680.00 40.00 48.00 27.00 660.00 <2.0 1300.00 51.30 7.61 <0.002 0.43 0.73 0.04 4.06 11.40 1.06 0.55 2.00 05.11.2019 0.69 7.95 39.70 2.80 3.90 3.30 3.30 330.00 42.00 52.00 32.00 900.00 53.00 1600.00 34.70 7.69 04.12.2019 1.04 7.90 109.00 4.00 5.16 8.00 7.70 646.00 34.00 51.00 28.00 830.00 200.00 1400.00 74.00 7.31 Lower avg. 5.40 7.91 51.53 8.67 13.48 4.36 4.02 630.58 50.33 64.50 32.25 932.50 100.75 1500.00 51.58 6.60 0.01 0.38 0.47 0.03 2.88 10.25 0.95 0.28 0.50 Upper avg.. 5.40 7.91 51.53 8.67 13.48 4.36 4.02 630.58 50.33 64.50 32.25 932.50 101.25 1500.00 51.67 6.60 0.01 0.38 0.47 0.03 2.88 10.25 0.95 0.28 1.25 Minimum 0.39 7.75 27.10 2.00 2.09 2.40 2.10 330.00 24.00 34.00 12.00 660.00 2.00 1100.00 1.00 4.46 0.00 0.26 0.17 0.02 1.90 6.50 0.59 0.15 1.00 Maximum 52.59 8.11 109.00 61.00 104.00 11.10 8.50 2114.00 140.00 170.00 58.00 1200.00 460.00 2000.00 190.00 7.69 0.02 0.52 0.73 0.04 4.06 14.60 1.48 0.55 2.00 More than 70% >LOD yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes no n 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 St.dev 14.88 0.11 22.19 16.56 28.63 2.64 2.08 489.07 31.65 36.73 13.10 183.66 135.88 255.84 47.11 0.93 0.01 0.12 0.28 0.01 0.90 3.53 0.41 0.18 0.50

Drammenselva Date Qs pH KOND TURB860 SPM TOC DOC Part. C PO4-P TOTP TDP NO3-N NH4-N TOTN Tot. Part. N SiO2 Ag As Pb Cd Cu Zn Ni Cr Hg DD.MM.YYYY [m3/s] [] [mS/m] [FNU] [mg/l] [mgC/l] [mgC/l] [µgC/l] [µgP/l] [µgP/l] [µgP/l] [µgN/l] [µgN/l] [µgN/l] [µgN/l] [mgSiO2/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [ng/l] 07.01.2019 309.32 7.03 3.59 0.58 0.75 3.20 3.20 105.00 <1.0 4.00 3.00 290.00 9.00 380.00 4.87 2.91 04.02.2019 311.30 7.11 3.58 0.75 0.77 2.90 2.80 133.00 2.00 5.00 2.00 220.00 14.00 350.00 8.63 2.76 <0.002 0.11 0.04 0.01 0.47 1.40 0.39 0.09 5.00 04.03.2019 332.83 7.10 4.45 0.94 1.05 2.80 2.80 124.00 1.00 4.00 2.00 350.00 10.00 490.00 9.34 3.00 02.04.2019 245.13 7.23 5.90 2.80 4.19 3.90 3.90 261.00 4.00 8.00 3.00 900.00 3.00 1000.00 24.00 3.77 06.05.2019 410.45 7.32 4.66 0.38 1.55 3.30 3.30 179.00 1.00 5.00 2.00 240.00 2.00 360.00 1.00 3.00 0.00 0.13 0.10 0.01 0.73 2.80 0.43 0.11 <1.0 15.05.2019 470.08 7.21 4.01 0.80 1.07 3.50 3.40 97.90 3.00 5.00 <1.0 360.00 4.00 440.00 <1.0 3.04 22.05.2019 690.61 7.15 4.20 3.30 3.59 3.60 3.50 372.00 5.00 9.00 1.00 520.00 9.00 730.00 <1.0 3.02 03.06.2019 553.30 7.18 3.70 1.10 1.36 3.40 3.30 155.00 3.00 6.00 1.00 260.00 2.00 380.00 13.50 2.89 12.06.2019 591.18 7.11 3.91 3.10 3.94 4.00 3.90 329.00 3.00 10.00 2.00 380.00 <2.0 510.00 <1.0 3.00 24.06.2019 413.30 7.52 5.20 0.59 1.25 3.90 3.90 256.00 <1.0 6.00 2.00 260.00 5.00 380.00 25.40 2.96 01.07.2019 245.46 6.99 3.98 0.67 1.47 4.00 3.90 220.00 2.00 5.00 3.00 240.00 14.00 370.00 <1.0 2.68 05.08.2019 191.90 7.21 3.54 0.64 0.73 3.40 3.20 193.00 2.00 4.00 1.00 150.00 19.00 290.00 21.90 2.53 <0.002 0.17 0.05 0.01 0.63 0.80 0.35 0.07 <1.0 02.09.2019 311.90 7.09 3.72 1.30 1.04 4.00 3.90 171.00 1.00 6.00 3.00 200.00 10.00 340.00 9.82 2.55 07.10.2019 303.22 7.03 3.91 0.82 0.96 4.10 4.00 138.00 2.00 4.00 2.00 270.00 21.00 450.00 16.50 2.72 <0.002 0.13 0.06 0.01 0.65 1.40 0.50 0.11 5.00 04.11.2019 366.83 7.15 4.05 0.94 1.18 3.90 3.70 151.00 1.00 4.00 <1.0 260.00 13.00 420.00 10.90 2.89 03.12.2019 313.94 7.13 4.35 0.74 0.80 4.10 4.00 127.00 2.00 4.00 2.00 320.00 8.00 480.00 8.31 3.15 Lower avg. 378.80 7.16 4.17 1.22 1.61 3.62 3.54 188.24 2.00 5.56 1.81 326.25 8.94 460.62 9.64 2.93 0.00 0.14 0.06 0.01 0.62 1.60 0.42 0.10 2.50 Upper avg.. 378.80 7.16 4.17 1.22 1.61 3.62 3.54 188.24 2.12 5.56 1.94 326.25 9.06 460.62 9.89 2.93 0.00 0.14 0.06 0.01 0.62 1.60 0.42 0.10 3.00 Minimum 191.90 6.99 3.54 0.38 0.73 2.80 2.80 97.90 1.00 4.00 1.00 150.00 2.00 290.00 1.00 2.53 0.00 0.11 0.04 0.01 0.47 0.80 0.35 0.07 1.00 Maximum 690.61 7.52 5.90 3.30 4.19 4.10 4.00 372.00 5.00 10.00 3.00 900.00 21.00 1000.00 25.40 3.77 0.00 0.17 0.10 0.01 0.73 2.80 0.50 0.11 5.00 More than 70% >LOD yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes no yes yes yes yes yes yes yes no n 16.00 16.00 16.00 16.00 16.00 16.00 16.00 16.00 16.00 16.00 16.00 16.00 16.00 16.00 16.00 16.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 St.dev 136.61 0.13 0.64 0.95 1.17 0.43 0.41 79.96 1.20 1.90 0.77 175.53 5.98 175.71 8.43 0.29 0.00 0.03 0.03 0.00 0.11 0.85 0.06 0.02 2.31

Numedalslågen Date Qs pH KOND TURB860 SPM TOC DOC Part. C PO4-P TOTP TDP NO3-N NH4-N TOTN Tot. Part. N SiO2 Ag As Pb Cd Cu Zn Ni Cr Hg DD.MM.YYYY [m3/s] [] [mS/m] [FNU] [mg/l] [mgC/l] [mgC/l] [µgC/l] [µgP/l] [µgP/l] [µgP/l] [µgN/l] [µgN/l] [µgN/l] [µgN/l] [mgSiO2/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [ng/l] 07.01.2019 86.09 6.88 3.33 4.50 4.93 3.00 2.90 471.00 10.00 14.00 3.00 250.00 61.00 380.00 <1.0 3.56 04.02.2019 65.74 6.84 4.71 2.70 4.37 3.40 3.30 711.00 4.00 10.00 2.00 620.00 290.00 860.00 61.40 4.50 0.01 0.34 1.07 0.05 2.28 16.70 0.65 0.49 1.00 04.03.2019 105.05 6.82 4.71 2.90 3.21 3.70 3.80 231.00 5.00 9.00 3.00 470.00 21.00 660.00 16.20 4.16 01.04.2019 215.73 6.85 3.79 6.20 8.32 4.80 4.80 500.00 10.00 15.00 3.00 460.00 2.00 630.00 <1.0 4.16 06.05.2019 144.90 6.64 2.35 1.50 3.79 4.20 4.10 312.00 2.00 7.00 3.00 130.00 6.00 280.00 20.80 3.06 0.01 0.16 0.34 0.02 0.67 3.60 0.33 0.15 4.00 03.06.2019 105.58 6.83 2.80 1.40 1.92 4.30 4.10 247.00 3.00 7.00 2.00 150.00 20.00 300.00 16.90 3.06 01.07.2019 85.16 7.02 2.94 1.30 3.48 3.70 3.60 215.00 3.00 6.00 2.00 130.00 22.00 250.00 16.00 2.64 05.08.2019 91.15 6.44 2.39 0.99 1.59 2.30 2.30 163.00 3.00 6.00 2.00 58.00 26.00 170.00 12.60 2.21 0.01 0.12 0.15 0.01 0.47 1.20 0.25 0.07 <1.0 02.09.2019 71.52 6.93 3.87 2.10 2.08 5.60 5.80 323.00 3.00 10.00 6.00 200.00 44.00 390.00 20.80 2.94 07.10.2019 86.23 6.88 3.88 2.50 2.80 5.60 5.50 218.00 4.00 9.00 3.00 260.00 30.00 530.00 1.00 3.66 0.00 0.19 0.23 0.01 0.85 3.70 0.40 0.18 2.00 04.11.2019 46.35 7.01 4.80 2.40 2.97 6.00 5.80 219.00 5.00 10.00 3.00 370.00 100.00 630.00 26.00 4.44 02.12.2019 60.20 6.92 5.35 4.80 8.12 6.60 6.30 575.00 14.00 22.00 6.00 600.00 3.00 870.00 40.40 5.38 Lower avg. 96.97 6.84 3.74 2.77 3.96 4.43 4.36 348.75 5.50 10.42 3.17 308.17 52.08 495.83 19.34 3.65 0.01 0.20 0.45 0.02 1.07 6.30 0.41 0.22 1.75 Upper avg.. 96.97 6.84 3.74 2.77 3.96 4.43 4.36 348.75 5.50 10.42 3.17 308.17 52.08 495.83 19.51 3.65 0.01 0.20 0.45 0.02 1.07 6.30 0.41 0.22 2.00 Minimum 46.35 6.44 2.35 0.99 1.59 2.30 2.30 163.00 2.00 6.00 2.00 58.00 2.00 170.00 1.00 2.21 0.00 0.12 0.15 0.01 0.47 1.20 0.25 0.07 1.00 Maximum 215.73 7.02 5.35 6.20 8.32 6.60 6.30 711.00 14.00 22.00 6.00 620.00 290.00 870.00 61.40 5.38 0.01 0.34 1.07 0.05 2.28 16.70 0.65 0.49 4.00 More than 70% >LOD yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes n 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 St.dev 45.21 0.16 1.00 1.61 2.21 1.31 1.28 173.94 3.75 4.62 1.40 191.63 79.86 236.05 17.43 0.91 0.00 0.10 0.42 0.02 0.82 7.03 0.17 0.18 1.41

Skienselva Date Qs pH KOND TURB860 SPM TOC DOC Part. C PO4-P TOTP TDP NO3-N NH4-N TOTN Tot. Part. N SiO2 Ag As Pb Cd Cu Zn Ni Cr Hg DD.MM.YYYY [m3/s] [] [mS/m] [FNU] [mg/l] [mgC/l] [mgC/l] [µgC/l] [µgP/l] [µgP/l] [µgP/l] [µgN/l] [µgN/l] [µgN/l] [µgN/l] [mgSiO2/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [ng/l] 14.01.2019 305.70 6.73 1.94 0.35 0.37 2.40 2.40 80.70 <1.0 3.00 2.00 170.00 8.00 230.00 4.18 2.21 05.02.2019 306.45 6.68 1.87 0.44 <0.67 2.30 2.30 98.70 1.00 3.00 2.00 130.00 6.00 220.00 5.05 2.23 <0.002 0.08 0.04 0.01 0.33 1.70 0.19 0.06 <1.0 11.03.2019 269.46 6.78 2.09 0.66 0.83 2.50 2.40 153.00 1.00 3.00 2.00 160.00 <2.0 260.00 11.80 2.40 01.04.2019 328.18 6.78 2.09 0.98 1.93 2.50 2.40 166.00 3.00 5.00 2.00 170.00 <2.0 250.00 <1.0 2.40 06.05.2019 301.62 6.74 1.95 0.41 0.42 2.20 2.20 79.40 <1.0 3.00 2.00 140.00 <2.0 210.00 <1.0 2.25 <0.002 0.08 0.04 0.01 0.34 2.10 0.15 0.07 <1.0 03.06.2019 280.15 6.90 2.13 0.47 0.73 2.60 2.50 155.00 2.00 4.00 <1.0 150.00 14.00 240.00 11.70 2.23 01.07.2019 192.34 6.87 1.99 0.54 0.53 2.80 2.70 162.00 <1.0 7.00 4.00 130.00 4.00 230.00 8.74 2.06 06.08.2019 306.84 6.19 1.88 0.31 <0.4 2.50 2.50 111.00 1.00 3.00 <1.0 97.00 20.00 180.00 12.30 1.87 <0.002 0.10 0.04 0.01 0.41 1.50 0.18 0.06 <1.0 03.09.2019 213.26 6.75 1.95 1.60 0.61 2.90 2.90 149.00 2.00 4.00 1.00 96.00 26.00 200.00 15.10 1.95 14.10.2019 428.71 6.70 1.95 0.58 0.77 2.90 2.90 151.00 2.00 3.00 <1.0 120.00 16.00 220.00 10.70 2.12 <0.002 0.09 0.05 0.01 0.67 1.90 0.34 0.07 4.00 04.11.2019 120.90 6.78 2.04 0.75 0.79 3.20 3.00 114.00 <1.0 4.00 <1.0 130.00 17.00 250.00 14.40 2.23 09.12.2019 167.97 6.54 2.02 0.66 0.59 2.80 2.80 110.00 <1.0 3.00 1.00 150.00 9.00 270.00 8.50 2.42 Lower avg. 268.46 6.70 1.99 0.65 0.63 2.63 2.58 127.48 1.00 3.75 1.33 136.92 10.00 230.00 8.54 2.20 0.00 0.09 0.04 0.01 0.44 1.80 0.22 0.06 1.00 Upper avg.. 268.46 6.70 1.99 0.65 0.72 2.63 2.58 127.48 1.42 3.75 1.67 136.92 10.50 230.00 8.71 2.20 0.00 0.09 0.04 0.01 0.44 1.80 0.22 0.06 1.75 Minimum 120.90 6.19 1.87 0.31 0.37 2.20 2.20 79.40 1.00 3.00 1.00 96.00 2.00 180.00 1.00 1.87 0.00 0.08 0.04 0.01 0.33 1.50 0.15 0.06 1.00 Maximum 428.71 6.90 2.13 1.60 1.93 3.20 3.00 166.00 3.00 7.00 4.00 170.00 26.00 270.00 15.10 2.42 0.00 0.10 0.05 0.01 0.67 2.10 0.34 0.07 4.00 More than 70% >LOD yes yes yes yes yes yes yes yes no yes no yes yes yes yes yes no yes yes yes yes yes yes yes no n 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 St.dev 82.85 0.19 0.08 0.35 0.41 0.29 0.27 31.89 0.67 1.22 0.89 24.81 7.99 25.94 4.87 0.17 0.00 0.01 0.00 0.00 0.16 0.26 0.09 0.01 1.50

Otra Date Qs pH KOND TURB860 SPM TOC DOC Part. C PO4-P TOTP TDP NO3-N NH4-N TOTN Tot. Part. N SiO2 Ag As Pb Cd Cu Zn Ni Cr Hg DD.MM.YYYY [m3/s] [] [mS/m] [FNU] [mg/l] [mgC/l] [mgC/l] [µgC/l] [µgP/l] [µgP/l] [µgP/l] [µgN/l] [µgN/l] [µgN/l] [µgN/l] [mgSiO2/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [ng/l] 07.01.2019 144.01 6.14 1.51 0.40 <0.5 2.70 2.60 173.00 <1.0 3.00 2.00 100.00 19.00 180.00 9.27 1.79 04.02.2019 137.64 6.21 1.49 0.34 0.62 2.30 2.30 130.00 1.00 3.00 1.00 76.00 17.00 160.00 6.96 1.68 <0.002 0.08 0.13 0.01 0.29 2.00 0.23 0.06 4.00 04.03.2019 138.74 6.28 1.73 0.68 0.79 2.90 2.80 4.96 1.00 2.00 2.00 96.00 26.00 230.00 1.00 1.90 01.04.2019 138.07 6.24 1.69 0.47 0.87 3.00 3.00 175.00 3.00 3.00 1.00 92.00 3.00 180.00 10.30 1.64 06.05.2019 74.65 6.39 1.56 0.60 1.26 2.40 2.20 295.00 <1.0 4.00 2.00 69.00 <2.0 170.00 <1.0 1.10 0.00 0.11 0.19 0.01 0.43 2.30 0.42 0.08 <1.0 03.06.2019 83.32 6.34 1.49 0.43 0.96 2.90 2.80 313.00 <1.0 4.00 1.00 84.00 7.00 180.00 24.30 1.27 01.07.2019 63.20 6.30 1.44 0.50 1.29 2.80 2.70 244.00 <1.0 3.00 2.00 61.00 <2.0 150.00 17.20 1.07 05.08.2019 78.89 6.05 1.42 0.47 1.69 3.50 3.40 175.00 2.00 5.00 1.00 57.00 24.00 190.00 11.30 1.15 0.01 0.15 0.23 0.02 0.54 2.60 0.41 0.09 <1.0 02.09.2019 115.76 6.02 1.45 0.56 1.24 5.00 5.00 311.00 <1.0 4.00 2.00 56.00 5.00 240.00 13.00 1.50 07.10.2019 166.51 6.09 1.48 0.56 1.27 4.40 4.40 316.00 2.00 4.00 <1.0 56.00 18.00 240.00 27.30 1.67 0.01 0.20 0.35 0.04 0.75 3.40 0.67 0.11 <1.0 04.11.2019 164.63 6.20 1.42 0.70 0.79 3.60 3.50 212.00 <1.0 3.00 <1.0 65.00 14.00 190.00 34.60 1.88 02.12.2019 220.62 6.13 1.42 0.57 0.69 4.10 4.10 219.00 2.00 3.00 1.00 76.00 17.00 210.00 12.60 1.96 Lower avg. 127.17 6.20 1.51 0.52 0.96 3.30 3.23 214.00 0.92 3.42 1.25 74.00 12.50 193.33 13.99 1.55 0.00 0.14 0.23 0.02 0.50 2.58 0.43 0.09 1.00 Upper avg.. 127.17 6.20 1.51 0.52 1.00 3.30 3.23 214.00 1.42 3.42 1.42 74.00 12.83 193.33 14.07 1.55 0.00 0.14 0.23 0.02 0.50 2.58 0.43 0.09 1.75 Minimum 63.20 6.02 1.42 0.34 0.50 2.30 2.20 4.96 1.00 2.00 1.00 56.00 2.00 150.00 1.00 1.07 0.00 0.08 0.13 0.01 0.29 2.00 0.23 0.06 1.00 Maximum 220.62 6.39 1.73 0.70 1.69 5.00 5.00 316.00 3.00 5.00 2.00 100.00 26.00 240.00 34.60 1.96 0.01 0.20 0.35 0.04 0.75 3.40 0.67 0.11 4.00 More than 70% >LOD yes yes yes yes yes yes yes yes no yes yes yes yes yes yes yes yes yes yes yes yes yes yes yes no n 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 St.dev 46.28 0.12 0.10 0.11 0.35 0.84 0.87 91.74 0.67 0.79 0.51 15.97 8.66 30.25 10.23 0.33 0.00 0.05 0.09 0.02 0.19 0.60 0.18 0.02 1.50

Orreelva Date Qs pH KOND TURB860 SPM TOC DOC Part. C PO4-P TOTP TDP NO3-N NH4-N TOTN Tot. Part. N SiO2 Ag As Pb Cd Cu Zn Ni Cr Hg DD.MM.YYYY [m3/s] [] [mS/m] [FNU] [mg/l] [mgC/l] [mgC/l] [µgC/l] [µgP/l] [µgP/l] [µgP/l] [µgN/l] [µgN/l] [µgN/l] [µgN/l] [mgSiO2/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [ng/l] 07.01.2019 10.00 7.67 19.60 13.00 24.90 9.50 5.20 2618.00 43.00 130.00 10.00 1300.00 5.00 2300.00 308.00 2.11 05.02.2019 8.92 7.57 19.90 10.00 27.40 8.90 5.50 3732.00 52.00 97.00 12.00 1500.00 4.00 2800.00 388.00 3.28 0.00 0.35 0.70 0.03 1.98 5.20 1.38 0.32 5.00 04.03.2019 2.96 7.59 19.50 6.00 7.77 7.90 5.29 1444.00 10.00 41.00 9.00 1500.00 <2.0 2400.00 206.00 1.64 01.04.2019 3.82 7.78 18.90 4.70 8.58 7.70 6.30 1760.00 12.00 42.00 5.00 1400.00 61.00 2000.00 201.00 0.10 06.05.2019 0.86 7.63 20.90 11.00 17.50 10.40 5.60 3360.00 17.00 63.00 8.00 430.00 24.00 1200.00 448.00 0.25 0.00 0.36 0.55 0.02 1.74 3.20 1.23 0.18 <1.0 03.06.2019 1.74 7.81 21.20 8.70 5.14 7.90 6.40 1860.00 9.00 54.00 6.00 <2.0 <2.0 550.00 515.00 0.24 01.07.2019 0.90 8.23 21.60 17.00 10.90 9.50 7.60 4414.00 6.00 82.00 7.00 <2.0 <2.0 890.00 663.00 0.77 05.08.2019 3.03 7.51 21.20 9.00 7.31 9.20 7.20 3041.00 12.00 64.00 7.00 <2.0 160.00 760.00 488.00 2.04 <0.002 0.47 0.06 0.00 0.89 0.72 1.17 0.04 <1.0 02.09.2019 7.78 7.77 21.90 14.00 15.80 12.00 10.70 4269.00 22.00 100.00 11.00 35.00 36.00 960.00 336.00 2.98 07.10.2019 2.78 7.63 18.70 16.00 10.40 8.10 6.80 2477.00 18.00 55.00 8.00 400.00 21.00 1300.00 520.00 3.66 0.00 0.32 0.09 0.00 1.46 1.60 1.14 0.13 <1.0 04.11.2019 3.25 7.67 19.30 19.00 8.25 7.50 6.20 3581.00 13.00 74.00 7.00 550.00 <2.0 1500.00 699.00 3.99 02.12.2019 2.09 7.73 20.40 25.00 10.80 8.30 5.90 4032.00 9.00 95.00 7.00 370.00 <2.0 1100.00 692.00 4.82 Lower avg. 4.01 7.72 20.26 12.78 12.90 8.91 6.56 3049.00 18.58 74.75 8.08 623.75 25.92 1480.00 455.33 2.16 0.00 0.38 0.35 0.01 1.52 2.68 1.23 0.17 1.25 Upper avg.. 4.01 7.72 20.26 12.78 12.90 8.91 6.56 3049.00 18.58 74.75 8.08 624.25 26.75 1480.00 455.33 2.16 0.00 0.38 0.35 0.01 1.52 2.68 1.23 0.17 2.00 Minimum 0.86 7.51 18.70 4.70 5.14 7.50 5.20 1444.00 6.00 41.00 5.00 2.00 2.00 550.00 201.00 0.10 0.00 0.32 0.06 0.00 0.89 0.72 1.14 0.04 1.00 Maximum 10.00 8.23 21.90 25.00 27.40 12.00 10.70 4414.00 52.00 130.00 12.00 1500.00 160.00 2800.00 699.00 4.82 0.00 0.47 0.70 0.03 1.98 5.20 1.38 0.32 5.00 More than 70% >LOD yes yes yes yes yes yes yes yes yes yes yes yes no yes yes yes yes yes yes yes yes yes yes yes no n 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 St.dev 3.11 0.19 1.09 5.81 7.11 1.32 1.50 1014.82 14.34 26.86 2.07 622.26 45.83 725.45 174.44 1.61 0.00 0.07 0.33 0.01 0.47 1.97 0.11 0.12 2.00

Vosso (Bolstadelvi) Date Qs pH KOND TURB860 SPM TOC DOC Part. C PO4-P TOTP TDP NO3-N NH4-N TOTN Tot. Part. N SiO2 Ag As Pb Cd Cu Zn Ni Cr Hg DD.MM.YYYY [m3/s] [] [mS/m] [FNU] [mg/l] [mgC/l] [mgC/l] [µgC/l] [µgP/l] [µgP/l] [µgP/l] [µgN/l] [µgN/l] [µgN/l] [µgN/l] [mgSiO2/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [ng/l] 07.01.2019 40.11 6.55 1.45 1.30 2.65 1.20 1.10 422.00 1.00 7.00 2.00 120.00 5.00 380.00 27.70 1.06 04.02.2019 6.69 6.50 1.42 <0.3 0.40 0.86 0.85 105.00 1.00 3.00 2.00 100.00 5.00 150.00 10.80 1.02 <0.002 0.06 0.03 0.00 0.26 0.66 0.25 0.04 <1.0 05.03.2019 28.88 6.55 1.83 0.41 0.56 1.10 1.10 103.00 1.00 2.00 2.00 160.00 <2.0 230.00 2.00 1.26 01.04.2019 95.88 6.46 1.85 0.59 0.51 1.10 1.10 106.00 1.00 3.00 <1.0 160.00 <2.0 220.00 8.04 1.17 06.05.2019 52.94 6.74 1.92 0.58 0.76 1.10 0.98 114.00 <1.0 4.00 2.00 160.00 7.00 220.00 24.40 1.30 0.00 0.07 0.06 0.01 0.35 0.98 0.28 0.04 3.00 03.06.2019 248.34 6.54 1.33 0.33 0.44 1.10 1.10 158.00 1.00 3.00 <1.0 81.00 <2.0 140.00 20.90 1.02 01.07.2019 95.35 6.50 1.08 0.32 0.96 1.30 1.20 119.00 <1.0 3.00 2.00 50.00 2.00 110.00 9.53 0.81 12.08.2019 69.37 6.65 1.15 0.52 0.64 1.30 1.40 184.00 2.00 4.00 <1.0 38.00 3.00 100.00 25.30 0.66 <0.002 0.10 0.08 0.00 0.37 0.58 0.29 0.06 <1.0 02.09.2019 275.19 6.40 1.21 0.70 1.03 2.00 1.90 226.00 <1.0 5.00 4.00 75.00 <2.0 160.00 21.00 1.08 07.10.2019 18.36 6.51 1.37 0.56 0.66 1.30 1.30 148.00 2.00 3.00 1.00 90.00 5.00 170.00 <1.0 1.08 <0.002 0.09 0.06 0.00 0.40 0.85 0.30 0.05 1.00 04.11.2019 22.73 6.47 1.41 0.49 1.29 1.30 1.30 213.00 2.00 4.00 <1.0 97.00 5.00 170.00 22.30 1.17 02.12.2019 7.91 6.45 1.52 <0.3 0.25 1.10 1.10 78.70 1.00 3.00 <1.0 130.00 7.00 180.00 2.02 1.08 Lower avg. 80.15 6.53 1.46 0.48 0.85 1.23 1.20 164.72 1.00 3.67 1.25 105.08 3.25 185.83 14.50 1.06 0.00 0.08 0.06 0.00 0.34 0.77 0.28 0.05 1.00 Upper avg.. 80.15 6.53 1.46 0.53 0.85 1.23 1.20 164.72 1.25 3.67 1.67 105.08 3.92 185.83 14.58 1.06 0.00 0.08 0.06 0.00 0.34 0.77 0.28 0.05 1.50 Minimum 6.69 6.40 1.08 0.30 0.25 0.86 0.85 78.70 1.00 2.00 1.00 38.00 2.00 100.00 1.00 0.66 0.00 0.06 0.03 0.00 0.26 0.58 0.25 0.04 1.00 Maximum 275.19 6.74 1.92 1.30 2.65 2.00 1.90 422.00 2.00 7.00 4.00 160.00 7.00 380.00 27.70 1.30 0.00 0.10 0.08 0.01 0.40 0.98 0.30 0.06 3.00 More than 70% >LOD yes yes yes yes yes yes yes yes yes yes no yes no yes yes yes no yes yes yes yes yes yes yes no n 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 St.dev 90.26 0.09 0.28 0.28 0.64 0.28 0.26 93.35 0.45 1.30 0.89 41.81 1.98 73.66 10.03 0.18 0.00 0.02 0.02 0.00 0.06 0.18 0.02 0.01 1.00

Orkla Date Qs pH KOND TURB860 SPM TOC DOC Part. C PO4-P TOTP TDP NO3-N NH4-N TOTN Tot. Part. N SiO2 Ag As Pb Cd Cu Zn Ni Cr Hg DD.MM.YYYY [m3/s] [] [mS/m] [FNU] [mg/l] [mgC/l] [mgC/l] [µgC/l] [µgP/l] [µgP/l] [µgP/l] [µgN/l] [µgN/l] [µgN/l] [µgN/l] [mgSiO2/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [ng/l] 09.01.2019 43.99 7.36 7.58 0.65 0.96 3.90 3.80 197.00 <1.0 5.00 3.00 360.00 4.00 440.00 <1.0 3.30 11.02.2019 44.16 7.47 6.65 0.56 2.64 1.90 1.90 214.00 2.00 5.00 1.00 230.00 7.00 340.00 <1.0 3.26 <0.002 0.07 0.03 0.03 3.09 7.40 0.68 0.22 <1.0 04.03.2019 63.07 7.49 7.29 0.64 0.63 2.60 2.70 350.00 1.00 3.00 2.00 220.00 <2.0 350.00 37.40 3.30 01.04.2019 42.04 7.46 8.93 1.10 1.13 3.50 3.40 216.00 2.00 4.00 2.00 320.00 <2.0 470.00 5.95 3.60 06.05.2019 62.27 7.43 6.84 0.69 0.74 3.40 3.30 150.00 <1.0 4.00 2.00 250.00 <2.0 400.00 <1.0 3.15 <0.002 0.12 0.02 0.04 7.02 12.90 0.83 0.24 2.00 02.06.2019 58.70 7.37 5.45 0.32 0.62 3.30 3.30 146.00 1.00 3.00 <1.0 150.00 <2.0 250.00 8.59 2.64 01.07.2019 88.52 7.46 4.97 0.82 1.73 4.20 4.20 182.00 1.00 7.00 2.00 97.00 <2.0 230.00 27.10 2.16 05.08.2019 30.80 7.72 7.35 0.35 <0.5 1.80 1.70 140.00 1.00 3.00 <1.0 190.00 <2.0 310.00 14.00 2.51 <0.002 0.10 0.01 0.01 2.03 3.90 0.62 0.10 <1.0 02.09.2019 23.88 7.58 7.00 0.39 1.02 2.40 2.30 177.00 <1.0 4.00 2.00 130.00 <2.0 230.00 1.48 2.27 07.10.2019 50.50 7.34 7.48 0.49 0.62 4.30 4.40 183.00 <1.0 3.00 2.00 250.00 2.00 420.00 <1.0 3.21 <0.002 0.14 0.02 0.05 7.17 16.50 0.82 0.24 1.00 04.11.2019 27.90 7.48 8.78 0.36 <0.29 3.90 3.80 130.00 2.00 3.00 1.00 290.00 5.00 440.00 12.20 3.64 02.12.2019 45.15 7.45 6.89 <0.3 0.47 1.90 1.80 89.20 1.00 2.00 <1.0 190.00 11.00 270.00 8.35 2.85 Lower avg. 48.42 7.47 7.10 0.53 0.88 3.09 3.05 181.18 0.92 3.83 1.42 223.08 2.42 345.83 9.59 2.99 0.00 0.11 0.02 0.03 4.83 10.18 0.74 0.20 0.75 Upper avg.. 48.42 7.47 7.10 0.56 0.95 3.09 3.05 181.18 1.25 3.83 1.67 223.08 3.58 345.83 9.92 2.99 0.00 0.11 0.02 0.03 4.83 10.18 0.74 0.20 1.25 Minimum 23.88 7.34 4.97 0.30 0.29 1.80 1.70 89.20 1.00 2.00 1.00 97.00 2.00 230.00 1.00 2.16 0.00 0.07 0.01 0.01 2.03 3.90 0.62 0.10 1.00 Maximum 88.52 7.72 8.93 1.10 2.64 4.30 4.40 350.00 2.00 7.00 3.00 360.00 11.00 470.00 37.40 3.64 0.00 0.14 0.03 0.05 7.17 16.50 0.83 0.24 2.00 More than 70% >LOD yes yes yes yes yes yes yes yes no yes yes yes no yes no yes no yes yes yes yes yes yes yes no n 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 St.dev 17.96 0.10 1.14 0.24 0.66 0.93 0.95 64.70 0.45 1.34 0.65 77.59 2.84 87.75 11.61 0.50 0.00 0.03 0.01 0.02 2.65 5.61 0.10 0.07 0.50

Vefsna Date Qs pH KOND TURB860 SPM TOC DOC Part. C PO4-P TOTP TDP NO3-N NH4-N TOTN Tot. Part. N SiO2 Ag As Pb Cd Cu Zn Ni Cr Hg DD.MM.YYYY [m3/s] [] [mS/m] [FNU] [mg/l] [mgC/l] [mgC/l] [µgC/l] [µgP/l] [µgP/l] [µgP/l] [µgN/l] [µgN/l] [µgN/l] [µgN/l] [mgSiO2/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [ng/l] 07.01.2019 123.41 7.41 5.21 0.45 0.88 1.80 1.70 288.00 1.00 3.00 2.00 47.00 3.00 97.00 33.40 1.37 05.02.2019 45.72 7.73 8.97 <0.3 <0.67 1.20 1.20 31.50 <1.0 2.00 2.00 110.00 <2.0 180.00 <1.0 2.14 <0.002 0.14 0.01 <0.003 0.27 0.18 0.23 0.06 <1.0 04.03.2019 94.72 7.56 8.90 0.39 0.46 1.60 1.60 142.00 <1.0 2.00 1.00 66.00 <2.0 130.00 8.75 1.81 01.04.2019 136.34 7.46 8.21 0.74 1.96 1.40 1.30 119.00 2.00 3.00 <1.0 43.00 <2.0 95.00 8.77 1.46 06.05.2019 150.35 7.61 7.00 0.36 1.19 1.30 1.30 7.29 <1.0 2.00 1.00 33.00 <2.0 100.00 <1.0 1.46 <0.002 0.08 0.03 0.00 0.34 0.50 0.32 0.07 <1.0 03.06.2019 259.76 7.50 5.31 <0.3 0.65 1.40 1.40 85.90 1.00 2.00 <1.0 24.00 <2.0 69.00 6.82 1.35 03.07.2019 258.21 7.38 3.74 0.59 1.30 1.30 1.20 58.00 2.00 10.00 1.00 12.00 <2.0 51.00 4.59 0.98 06.08.2019 57.66 7.39 4.68 <0.3 <0.5 0.61 0.58 52.40 1.00 2.00 <1.0 22.00 3.00 55.00 3.52 0.81 <0.002 0.11 0.02 <0.003 0.20 <0.15 0.20 0.04 <1.0 09.09.2019 117.23 7.47 5.14 <0.3 0.65 1.90 1.90 141.00 1.00 2.00 1.00 12.00 <2.0 64.00 12.90 1.22 02.10.2019 78.48 7.54 6.39 <0.3 <0.36 1.40 1.30 69.00 1.00 1.00 <1.0 30.00 3.00 94.00 <1.0 1.41 <0.002 0.10 0.01 <0.003 0.27 0.20 0.25 0.07 2.00 04.11.2019 72.23 7.61 7.94 <0.3 <0.38 1.70 1.70 44.10 <1.0 1.00 <1.0 50.00 4.00 120.00 5.87 1.70 03.12.2019 48.36 7.63 9.55 <0.3 <0.4 1.20 1.30 29.50 <1.0 <1.0 <1.0 110.00 4.00 180.00 4.56 2.10 Lower avg. 120.21 7.52 6.75 0.21 0.59 1.40 1.37 88.97 0.75 2.50 0.67 46.58 1.42 102.92 7.43 1.48 0.00 0.11 0.02 0.00 0.27 0.22 0.25 0.06 0.50 Upper avg.. 120.21 7.52 6.75 0.39 0.78 1.40 1.37 88.97 1.17 2.58 1.17 46.58 2.58 102.92 7.68 1.48 0.00 0.11 0.02 0.00 0.27 0.26 0.25 0.06 1.25 Minimum 45.72 7.38 3.74 0.30 0.36 0.61 0.58 7.29 1.00 1.00 1.00 12.00 2.00 51.00 1.00 0.81 0.00 0.08 0.01 0.00 0.20 0.15 0.20 0.04 1.00 Maximum 259.76 7.73 9.55 0.74 1.96 1.90 1.90 288.00 2.00 10.00 2.00 110.00 4.00 180.00 33.40 2.14 0.00 0.14 0.03 0.00 0.34 0.50 0.32 0.07 2.00 More than 70% >LOD yes yes yes no no yes yes yes no yes no yes no yes yes yes no yes yes no yes yes yes yes no n 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 St.dev 73.25 0.11 1.94 0.14 0.48 0.34 0.34 76.43 0.39 2.43 0.39 33.59 0.79 43.43 8.87 0.40 0.00 0.02 0.01 0.00 0.06 0.16 0.05 0.01 0.50

Altaelva Date Qs pH KOND TURB860 SPM TOC DOC Part. C PO4-P TOTP TDP NO3-N NH4-N TOTN Tot. Part. N SiO2 Ag As Pb Cd Cu Zn Ni Cr Hg DD.MM.YYYY [m3/s] [] [mS/m] [FNU] [mg/l] [mgC/l] [mgC/l] [µgC/l] [µgP/l] [µgP/l] [µgP/l] [µgN/l] [µgN/l] [µgN/l] [µgN/l] [mgSiO2/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [ng/l] 07.01.2019 32.98 7.47 7.48 0.76 1.61 2.60 2.60 70.50 2.00 5.00 3.00 69.00 <2.0 150.00 <1.0 5.19 03.02.2019 28.10 7.53 7.57 0.39 0.53 2.50 2.60 65.00 2.00 4.00 3.00 55.00 <2.0 150.00 4.78 5.70 <0.002 0.08 0.01 <0.003 0.38 <0.15 0.21 0.16 <1.0 04.03.2019 26.24 7.43 7.95 <0.3 0.41 2.50 2.50 102.00 3.00 5.00 4.00 46.00 <2.0 170.00 8.52 5.94 01.04.2019 26.11 7.61 8.28 0.69 0.64 2.40 2.30 121.00 2.00 4.00 2.00 62.00 <2.0 150.00 <1.0 6.21 06.05.2019 132.83 7.61 9.04 <0.3 1.36 2.50 2.40 128.00 <1.0 5.00 2.00 61.00 <2.0 170.00 19.10 6.11 <0.002 0.11 0.01 <0.003 0.40 0.18 0.20 0.19 <1.0 03.06.2019 460.12 7.44 10.80 1.90 7.93 4.10 3.10 240.00 2.00 8.00 6.00 13.00 <2.0 100.00 <1.0 4.39 09.07.2019 149.06 7.46 5.38 <0.3 0.88 3.20 3.20 209.00 2.00 3.00 2.00 18.00 <2.0 110.00 12.90 3.51 06.08.2019 43.51 7.48 6.13 <0.3 0.57 3.00 3.00 85.70 2.00 5.00 2.00 21.00 <2.0 140.00 <1.0 3.45 <0.002 0.09 0.02 <0.003 0.53 0.18 0.23 0.18 <1.0 03.09.2019 94.97 7.35 5.60 0.61 7.21 3.50 3.50 226.00 3.00 5.00 2.00 25.00 <2.0 130.00 30.40 3.15 06.10.2019 94.50 7.53 6.39 <0.3 0.59 3.00 3.20 111.00 1.00 3.00 1.00 18.00 6.00 110.00 12.10 3.56 0.01 0.10 0.01 <0.003 0.42 <0.15 0.25 0.15 1.00 04.11.2019 45.22 7.53 8.42 0.30 0.30 2.70 2.60 35.20 2.00 4.00 2.00 60.00 <2.0 160.00 12.70 3.86 03.12.2019 37.24 7.37 11.10 <0.3 7.17 2.40 2.40 70.40 4.00 7.00 6.00 66.00 5.00 210.00 8.02 4.80 Lower avg. 97.57 7.48 7.84 0.39 2.43 2.87 2.78 121.98 2.08 4.83 2.92 42.83 0.92 145.83 9.04 4.66 0.00 0.09 0.01 0.00 0.43 0.09 0.22 0.17 0.25 Upper avg.. 97.57 7.48 7.84 0.54 2.43 2.87 2.78 121.98 2.17 4.83 2.92 42.83 2.58 145.83 9.38 4.66 0.00 0.09 0.01 0.00 0.43 0.17 0.22 0.17 1.00 Minimum 26.11 7.35 5.38 0.30 0.30 2.40 2.30 35.20 1.00 3.00 1.00 13.00 2.00 100.00 1.00 3.15 0.00 0.08 0.01 0.00 0.38 0.15 0.20 0.15 1.00 Maximum 460.12 7.61 11.10 1.90 7.93 4.10 3.50 240.00 4.00 8.00 6.00 69.00 6.00 210.00 30.40 6.21 0.01 0.11 0.02 0.00 0.53 0.18 0.25 0.19 1.00 More than 70% >LOD yes yes yes no yes yes yes yes yes yes yes yes no yes no yes no yes yes no yes no yes yes no n 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 St.dev 122.05 0.08 1.85 0.46 3.05 0.52 0.40 67.61 0.83 1.47 1.62 21.93 1.38 30.88 8.90 1.15 0.00 0.01 0.00 0.00 0.07 0.02 0.02 0.02 0.00

Bjerkreimselva Date Qs pH KOND TURB860 SPM TOC DOC Part. C PO4-P TOTP TDP NO3-N NH4-N TOTN Tot. Part. N SiO2 Ag As Pb Cd Cu Zn Ni Cr Hg DD.MM.YYYY [m3/s] [] [mS/m] [FNU] [mg/l] [mgC/l] [mgC/l] [µgC/l] [µgP/l] [µgP/l] [µgP/l] [µgN/l] [µgN/l] [µgN/l] [µgN/l] [mgSiO2/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [ng/l] 09.01.2019 60.18 6.39 3.18 0.42 0.55 1.30 1.30 117.00 <1.0 3.00 2.00 380.00 5.00 420.00 9.28 1.53 04.02.2019 21.83 6.52 3.58 0.34 0.59 1.20 1.20 152.00 2.00 5.00 1.00 410.00 <2.0 500.00 12.40 1.83 <0.002 0.09 0.17 0.02 0.27 2.50 0.18 0.07 <1.0 06.03.2019 34.32 6.45 3.47 0.41 0.57 1.30 1.30 196.00 <1.0 3.00 2.00 340.00 5.00 470.00 17.20 1.63 09.04.2019 39.74 6.61 3.67 0.30 0.64 1.40 1.50 273.00 <1.0 4.00 2.00 360.00 7.00 450.00 29.00 1.49 08.05.2019 24.27 6.54 3.40 <0.3 <0.53 1.20 1.10 85.80 <1.0 3.00 2.00 260.00 <2.0 370.00 11.60 1.38 <0.002 0.07 0.12 0.01 0.20 1.90 0.08 0.05 <1.0 19.06.2019 32.26 6.57 3.30 <0.3 <0.29 1.30 1.30 294.00 <1.0 5.00 3.00 330.00 10.00 420.00 55.40 1.36 02.07.2019 26.84 6.74 3.38 <0.3 <0.38 1.40 1.30 118.00 2.00 4.00 2.00 290.00 <2.0 380.00 <1.0 1.22 06.08.2019 29.89 6.15 3.48 0.66 0.70 2.10 2.10 310.00 3.00 7.00 2.00 340.00 8.00 480.00 23.40 1.26 <0.002 0.12 0.17 0.01 0.37 2.60 0.20 0.07 <1.0 04.09.2019 47.91 6.45 3.06 0.42 0.83 2.10 2.00 236.00 1.00 5.00 3.00 300.00 3.00 420.00 18.10 1.39 02.10.2019 39.94 6.62 3.39 0.64 0.47 1.70 1.60 189.00 2.00 4.00 1.00 300.00 9.00 450.00 12.10 1.47 0.00 0.12 0.28 0.03 3.56 13.30 1.25 0.19 <1.0 05.11.2019 25.68 6.60 3.54 0.35 0.24 1.60 1.60 73.70 2.00 3.00 1.00 370.00 <2.0 480.00 11.50 1.78 04.12.2019 22.85 6.68 3.56 <0.3 <0.33 1.60 1.50 101.00 <1.0 3.00 <1.0 360.00 <2.0 480.00 10.50 1.88 Lower avg. 33.81 6.53 3.42 0.29 0.38 1.52 1.48 178.79 1.00 4.08 1.75 336.67 3.92 443.33 17.54 1.52 0.00 0.10 0.18 0.02 1.10 5.08 0.43 0.10 0.00 Upper avg.. 33.81 6.53 3.42 0.39 0.51 1.52 1.48 178.79 1.50 4.08 1.83 336.67 4.75 443.33 17.62 1.52 0.00 0.10 0.18 0.02 1.10 5.08 0.43 0.10 1.00 Minimum 21.83 6.15 3.06 0.30 0.24 1.20 1.10 73.70 1.00 3.00 1.00 260.00 2.00 370.00 1.00 1.22 0.00 0.07 0.12 0.01 0.20 1.90 0.08 0.05 1.00 Maximum 60.18 6.74 3.67 0.66 0.83 2.10 2.10 310.00 3.00 7.00 3.00 410.00 10.00 500.00 55.40 1.88 0.00 0.12 0.28 0.03 3.56 13.30 1.25 0.19 1.00 More than 70% >LOD yes yes yes no no yes yes yes no yes yes yes no yes yes yes no yes yes yes yes yes yes yes no n 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 St.dev 11.50 0.16 0.17 0.13 0.18 0.32 0.31 83.67 0.67 1.24 0.72 42.92 3.05 41.63 13.88 0.22 0.00 0.03 0.07 0.01 1.64 5.49 0.55 0.06 0.00

100

Vikedalselva Date Qs pH KOND TURB860 SPM TOC DOC Part. C PO4-P TOTP TDP NO3-N NH4-N TOTN Tot. Part. N SiO2 Ag As Pb Cd Cu Zn Ni Cr Hg DD.MM.YYYY [m3/s] [] [mS/m] [FNU] [mg/l] [mgC/l] [mgC/l] [µgC/l] [µgP/l] [µgP/l] [µgP/l] [µgN/l] [µgN/l] [µgN/l] [µgN/l] [mgSiO2/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [ng/l] 07.01.2019 8.22 6.43 2.48 0.33 <0.5 1.10 1.10 103.00 <1.0 3.00 2.00 220.00 10.00 430.00 8.54 0.96 04.02.2019 4.81 6.72 3.47 0.35 <0.67 0.86 0.82 110.00 1.00 4.00 2.00 350.00 9.00 430.00 6.98 1.57 <0.002 0.25 0.07 0.01 0.31 1.80 0.32 0.04 <1.0 04.03.2019 8.30 6.43 2.78 0.44 0.89 1.10 1.00 139.00 2.00 3.00 1.00 200.00 3.00 290.00 10.70 0.85 01.04.2019 12.03 6.39 2.44 0.38 1.43 1.20 1.10 305.00 2.00 4.00 1.00 180.00 6.00 260.00 25.40 0.95 06.05.2019 4.70 6.64 2.48 0.33 0.44 0.90 0.81 132.00 <1.0 2.00 1.00 140.00 <2.0 200.00 <1.0 0.86 <0.002 0.14 0.07 0.01 0.25 1.40 0.21 0.04 <1.0 03.06.2019 11.31 6.69 2.32 0.38 0.77 1.60 1.50 239.00 2.00 5.00 <1.0 180.00 4.00 150.00 42.70 0.78 01.07.2019 6.80 6.69 2.73 5.60 7.27 2.50 2.30 628.00 6.00 21.00 5.00 280.00 53.00 470.00 57.30 0.82 05.08.2019 5.36 6.75 2.48 <0.3 <0.5 1.60 1.50 159.00 2.00 4.00 <1.0 210.00 <2.0 310.00 10.90 0.91 <0.002 0.32 0.10 0.01 0.40 0.89 0.24 0.04 <1.0 02.09.2019 22.63 6.24 1.79 0.54 0.72 2.00 2.00 225.00 1.00 4.00 2.00 140.00 <2.0 260.00 19.80 0.78 07.10.2019 5.71 6.48 2.75 0.30 0.95 1.20 1.10 211.00 2.00 3.00 <1.0 210.00 7.00 300.00 23.10 1.19 <0.002 0.34 0.08 0.01 0.41 4.30 0.41 0.04 <1.0 04.11.2019 6.18 6.69 2.87 0.49 1.28 1.20 1.10 274.00 <1.0 3.00 <1.0 190.00 18.00 310.00 22.80 1.36 02.12.2019 3.58 6.77 3.06 0.32 0.40 1.10 1.10 110.00 1.00 2.00 1.00 280.00 9.00 340.00 13.10 1.45 Lower avg. 8.30 6.58 2.64 0.79 1.18 1.36 1.29 219.58 1.58 4.83 1.25 215.00 9.92 312.50 20.11 1.04 0.00 0.26 0.08 0.01 0.34 2.10 0.29 0.04 0.00 Upper avg.. 8.30 6.58 2.64 0.81 1.32 1.36 1.29 219.58 1.83 4.83 1.58 215.00 10.42 312.50 20.19 1.04 0.00 0.26 0.08 0.01 0.34 2.10 0.29 0.04 1.00 Minimum 3.58 6.24 1.79 0.30 0.40 0.86 0.81 103.00 1.00 2.00 1.00 140.00 2.00 150.00 1.00 0.78 0.00 0.14 0.07 0.01 0.25 0.89 0.21 0.04 1.00 Maximum 22.63 6.77 3.47 5.60 7.27 2.50 2.30 628.00 6.00 21.00 5.00 350.00 53.00 470.00 57.30 1.57 0.00 0.34 0.10 0.01 0.41 4.30 0.41 0.04 1.00 More than 70% >LOD yes yes yes yes yes yes yes yes yes yes no yes yes yes yes yes no yes yes yes yes yes yes yes no n 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 St.dev 5.21 0.17 0.42 1.51 1.90 0.48 0.46 145.54 1.40 5.17 1.16 61.27 14.19 94.59 16.04 0.28 0.00 0.09 0.01 0.00 0.08 1.51 0.09 0.00 0.00

101

Nausta Date Qs pH KOND TURB860 SPM TOC DOC Part. C PO4-P TOTP TDP NO3-N NH4-N TOTN Tot. Part. N SiO2 Ag As Pb Cd Cu Zn Ni Cr Hg DD.MM.YYYY [m3/s] [] [mS/m] [FNU] [mg/l] [mgC/l] [mgC/l] [µgC/l] [µgP/l] [µgP/l] [µgP/l] [µgN/l] [µgN/l] [µgN/l] [µgN/l] [mgSiO2/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [ng/l] 07.01.2019 30.44 6.10 1.74 <0.3 1.04 1.30 1.30 143.00 <1.0 3.00 2.00 100.00 3.00 150.00 11.40 1.23 05.02.2019 10.04 6.40 2.13 0.46 <0.67 1.10 1.00 63.50 1.00 3.00 2.00 160.00 <2.0 220.00 <1.0 2.23 <0.002 <0.025 0.02 0.00 0.17 1.10 0.11 0.03 <1.0 04.03.2019 18.41 6.48 2.12 0.58 <0.5 1.60 1.60 93.80 1.00 2.00 2.00 93.00 <2.0 170.00 5.81 1.44 03.04.2019 23.23 6.35 2.05 0.36 <0.3 1.20 1.10 129.00 1.00 3.00 <1.0 110.00 <2.0 150.00 <1.0 1.20 13.05.2019 13.24 6.48 1.74 0.47 0.36 1.30 1.20 86.30 2.00 2.00 <1.0 50.00 7.00 98.00 <1.0 0.85 <0.002 0.04 0.04 0.00 0.14 0.66 <0.04 0.04 <1.0 04.06.2019 22.77 6.23 1.29 0.76 2.51 2.60 2.60 331.00 3.00 8.00 1.00 55.00 <2.0 140.00 24.70 0.90 01.07.2019 26.04 6.18 1.43 1.30 3.68 4.80 4.70 777.00 18.00 28.00 13.00 180.00 41.00 380.00 66.70 1.04 29.08.2019 25.33 5.53 1.09 0.73 1.41 5.40 5.10 358.00 4.00 12.00 5.00 5.00 <2.0 180.00 8.85 1.08 <0.002 0.07 0.15 0.01 0.37 1.20 0.16 0.10 <1.0 10.09.2019 19.44 6.44 1.63 <0.3 0.71 2.50 2.50 112.00 2.00 6.00 2.00 110.00 <2.0 210.00 13.80 1.22 07.10.2019 13.79 6.28 1.52 1.60 4.26 1.50 1.40 293.00 5.00 7.00 2.00 50.00 <2.0 130.00 9.65 1.34 <0.002 0.06 0.10 0.00 0.24 1.10 0.13 0.09 1.00 05.11.2019 12.15 6.56 2.18 <0.3 0.52 1.60 1.60 88.30 2.00 5.00 2.00 180.00 2.00 280.00 <1.0 1.99 09.12.2019 37.82 6.02 1.83 <0.3 <0.5 2.50 2.50 127.00 2.00 5.00 3.00 110.00 3.00 240.00 11.30 1.82 Lower avg. 21.06 6.25 1.73 0.52 1.21 2.28 2.22 216.83 3.42 7.00 2.83 100.25 4.67 195.67 12.68 1.36 0.00 0.04 0.08 0.00 0.23 1.02 0.10 0.06 0.25 Upper avg.. 21.06 6.25 1.73 0.62 1.37 2.28 2.22 216.83 3.50 7.00 3.00 100.25 5.83 195.67 13.02 1.36 0.00 0.05 0.08 0.00 0.23 1.02 0.11 0.06 1.00 Minimum 10.04 5.53 1.09 0.30 0.30 1.10 1.00 63.50 1.00 2.00 1.00 5.00 2.00 98.00 1.00 0.85 0.00 0.03 0.02 0.00 0.14 0.66 0.04 0.03 1.00 Maximum 37.82 6.56 2.18 1.60 4.26 5.40 5.10 777.00 18.00 28.00 13.00 180.00 41.00 380.00 66.70 2.23 0.00 0.07 0.15 0.01 0.37 1.20 0.16 0.10 1.00 More than 70% >LOD yes yes yes no no yes yes yes yes yes yes yes no yes no yes no yes yes yes yes yes yes yes no n 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 St.dev 8.22 0.28 0.35 0.42 1.36 1.42 1.38 203.82 4.74 7.22 3.33 54.60 11.17 77.39 18.30 0.44 0.00 0.02 0.06 0.00 0.10 0.24 0.05 0.03 0.00

102

Driva Date Qs pH KOND TURB860 SPM TOC DOC Part. C PO4-P TOTP TDP NO3-N NH4-N TOTN Tot. Part. N SiO2 Ag As Pb Cd Cu Zn Ni Cr Hg DD.MM.YYYY [m3/s] [] [mS/m] [FNU] [mg/l] [mgC/l] [mgC/l] [µgC/l] [µgP/l] [µgP/l] [µgP/l] [µgN/l] [µgN/l] [µgN/l] [µgN/l] [mgSiO2/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [ng/l] 07.01.2019 127.45 7.03 4.22 0.40 0.77 2.10 2.10 117.00 2.00 4.00 4.00 300.00 <2.0 370.00 9.38 3.43 08.02.2019 32.72 7.05 3.66 <0.3 <1.0 0.95 0.97 56.40 <1.0 2.00 <1.0 170.00 <2.0 230.00 <1.0 2.96 <0.002 0.04 0.01 <0.003 0.44 0.17 0.15 0.10 <1.0 03.03.2019 102.09 7.17 4.74 0.73 <0.65 1.40 1.30 67.30 1.00 2.00 2.00 230.00 <2.0 330.00 5.37 3.36 01.04.2019 90.88 7.14 4.25 0.50 1.25 1.70 1.50 156.00 2.00 4.00 1.00 190.00 <2.0 280.00 15.80 2.96 06.05.2019 142.43 7.24 4.12 0.61 1.71 1.60 1.50 89.90 <1.0 3.00 1.00 150.00 <2.0 230.00 11.20 3.02 <0.002 0.03 0.01 <0.003 0.81 2.40 0.20 0.12 2.00 03.06.2019 120.67 7.13 3.62 <0.3 0.60 1.20 1.10 63.50 1.00 1.00 <1.0 110.00 <2.0 170.00 5.35 3.13 01.07.2019 103.87 7.13 2.18 0.48 1.96 0.76 0.69 118.00 2.00 3.00 1.00 37.00 <2.0 73.00 6.03 2.03 05.08.2019 56.35 7.23 3.58 <0.3 <0.33 0.64 0.65 77.50 1.00 2.00 <1.0 77.00 <2.0 120.00 4.06 2.49 <0.002 0.04 0.01 <0.003 0.39 0.18 0.11 0.10 <1.0 02.09.2019 52.59 7.15 3.35 <0.3 <0.4 0.90 0.80 68.50 <1.0 2.00 3.00 58.00 <2.0 100.00 <1.0 2.74 02.10.2019 130.94 7.02 3.21 <0.3 0.60 1.10 1.10 116.00 1.00 2.00 <1.0 110.00 3.00 190.00 15.50 2.89 <0.002 0.03 0.02 <0.003 0.60 0.37 0.17 0.15 2.00 04.11.2019 49.57 7.28 5.28 <0.3 0.42 1.20 1.20 121.00 <1.0 2.00 <1.0 250.00 7.00 350.00 16.20 4.18 02.12.2019 33.31 7.15 5.14 <0.3 <0.26 0.76 0.78 56.70 2.00 5.00 2.00 290.00 4.00 340.00 <1.0 5.42 Lower avg. 86.91 7.14 3.95 0.23 0.61 1.19 1.14 92.32 1.00 2.67 1.17 164.33 1.17 231.92 7.41 3.22 0.00 0.04 0.01 0.00 0.56 0.78 0.16 0.12 1.00 Upper avg.. 86.91 7.14 3.95 0.40 0.83 1.19 1.14 92.32 1.33 2.67 1.58 164.33 2.67 231.92 7.66 3.22 0.00 0.04 0.01 0.00 0.56 0.78 0.16 0.12 1.50 Minimum 32.72 7.02 2.18 0.30 0.26 0.64 0.65 56.40 1.00 1.00 1.00 37.00 2.00 73.00 1.00 2.03 0.00 0.03 0.01 0.00 0.39 0.17 0.11 0.10 1.00 Maximum 142.43 7.28 5.28 0.73 1.96 2.10 2.10 156.00 2.00 5.00 4.00 300.00 7.00 370.00 16.20 5.42 0.00 0.04 0.02 0.00 0.81 2.40 0.20 0.15 2.00 More than 70% >LOD yes yes yes no no yes yes yes no yes no yes no yes yes yes no yes yes no yes yes yes yes no n 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 St.dev 40.06 0.08 0.87 0.15 0.55 0.44 0.42 32.36 0.49 1.15 1.00 89.34 1.50 103.31 5.84 0.87 0.00 0.00 0.00 0.00 0.19 1.08 0.04 0.02 0.58

103

Nidelva (Tr.heim) Date Qs pH KOND TURB860 SPM TOC DOC Part. C PO4-P TOTP TDP NO3-N NH4-N TOTN Tot. Part. N SiO2 Ag As Pb Cd Cu Zn Ni Cr Hg DD.MM.YYYY [m3/s] [] [mS/m] [FNU] [mg/l] [mgC/l] [mgC/l] [µgC/l] [µgP/l] [µgP/l] [µgP/l] [µgN/l] [µgN/l] [µgN/l] [µgN/l] [mgSiO2/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [ng/l] 07.01.2019 162.39 7.16 4.03 2.90 3.23 3.30 3.30 131.00 3.00 6.00 3.00 160.00 6.00 250.00 8.49 2.31 06.02.2019 39.30 7.14 3.20 0.76 1.19 2.40 2.40 101.00 2.00 4.00 2.00 87.00 3.00 170.00 4.62 2.01 <0.002 0.08 0.03 <0.003 0.61 1.50 0.71 0.19 <1.0 04.03.2019 69.45 7.23 3.86 0.96 0.83 2.60 2.60 79.40 2.00 3.00 1.00 110.00 <2.0 210.00 <1.0 2.10 01.04.2019 68.36 7.19 3.90 1.30 1.55 2.70 2.60 140.00 3.00 4.00 <1.0 120.00 <2.0 210.00 13.50 2.16 13.05.2019 134.00 7.12 3.42 1.10 1.22 2.50 2.50 105.00 2.00 3.00 2.00 110.00 2.00 170.00 <1.0 1.98 <0.002 0.08 0.03 <0.003 0.60 0.49 0.60 0.19 <1.0 03.06.2019 175.04 7.20 3.23 0.30 0.85 2.50 2.50 113.00 1.00 3.00 1.00 87.00 <2.0 160.00 15.20 2.01 01.07.2019 115.92 7.32 3.88 4.40 5.39 3.10 3.10 190.00 6.00 9.00 4.00 110.00 2.00 230.00 <1.0 2.10 05.08.2019 34.28 7.33 3.38 0.37 <0.5 2.50 2.50 119.00 2.00 3.00 <1.0 50.00 3.00 130.00 11.30 1.72 <0.002 0.10 0.03 <0.003 0.67 0.54 0.64 0.16 <1.0 02.09.2019 54.48 7.20 3.23 0.41 0.69 2.60 2.60 142.00 <1.0 3.00 3.00 50.00 3.00 140.00 19.50 1.61 02.10.2019 79.26 7.23 3.62 2.80 3.84 3.00 3.00 200.00 4.00 5.00 1.00 79.00 7.00 190.00 15.50 2.27 <0.002 0.12 0.07 0.00 0.92 1.10 1.02 0.52 <1.0 04.11.2019 48.86 7.07 3.20 0.34 0.59 2.70 2.70 70.90 <1.0 3.00 <1.0 77.00 8.00 160.00 7.58 1.96 02.12.2019 31.04 7.11 3.27 0.33 1.07 2.70 2.70 58.20 2.00 3.00 <1.0 92.00 12.00 170.00 23.30 1.91 Lower avg. 84.36 7.19 3.52 1.33 1.70 2.72 2.71 120.79 2.25 4.08 1.42 94.33 3.83 182.50 9.92 2.01 0.00 0.10 0.04 0.00 0.70 0.91 0.74 0.27 0.00 Upper avg.. 84.36 7.19 3.52 1.33 1.75 2.72 2.71 120.79 2.42 4.08 1.75 94.33 4.33 182.50 10.17 2.01 0.00 0.10 0.04 0.00 0.70 0.91 0.74 0.27 1.00 Minimum 31.04 7.07 3.20 0.30 0.50 2.40 2.40 58.20 1.00 3.00 1.00 50.00 2.00 130.00 1.00 1.61 0.00 0.08 0.03 0.00 0.60 0.49 0.60 0.16 1.00 Maximum 175.04 7.33 4.03 4.40 5.39 3.30 3.30 200.00 6.00 9.00 4.00 160.00 12.00 250.00 23.30 2.31 0.00 0.12 0.07 0.00 0.92 1.50 1.02 0.52 1.00 More than 70% >LOD yes yes yes yes yes yes yes yes yes yes no yes yes yes yes yes no yes yes no yes yes yes yes no n 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 St.dev 50.28 0.08 0.32 1.33 1.55 0.28 0.28 43.50 1.44 1.83 1.06 30.58 3.23 36.21 7.50 0.20 0.00 0.02 0.02 0.00 0.15 0.48 0.19 0.17 0.00

104

Målselva v/gml E6-brua Date Qs pH KOND TURB860 SPM TOC DOC Part. C PO4-P TOTP TDP NO3-N NH4-N TOTN Tot. Part. N SiO2 Ag As Pb Cd Cu Zn Ni Cr Hg DD.MM.YYYY [m3/s] [] [mS/m] [FNU] [mg/l] [mgC/l] [mgC/l] [µgC/l] [µgP/l] [µgP/l] [µgP/l] [µgN/l] [µgN/l] [µgN/l] [µgN/l] [mgSiO2/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [ng/l] 07.01.2019 81.83 7.51 7.52 0.72 1.50 1.40 1.30 465.00 <1.0 5.00 2.00 98.00 42.00 290.00 76.50 2.57 04.02.2019 47.70 7.57 8.08 4.10 3.44 0.94 1.00 101.00 4.00 5.00 2.00 78.00 6.00 140.00 6.74 2.66 <0.002 0.06 0.09 <0.003 0.69 0.93 0.48 0.22 <1.0 04.03.2019 120.56 7.54 9.19 0.89 1.14 1.70 1.60 213.00 1.00 5.00 2.00 130.00 3.00 240.00 19.00 3.00 01.04.2019 76.17 7.52 8.12 0.70 0.90 1.20 1.10 160.00 2.00 5.00 2.00 240.00 2.00 240.00 20.70 2.61 10.05.2019 152.98 7.59 8.72 3.20 4.91 1.60 1.50 140.00 6.00 7.00 <1.0 110.00 <2.0 200.00 <1.0 2.89 <0.002 0.06 0.09 <0.003 0.67 0.72 0.53 0.27 <1.0 03.06.2019 424.73 7.46 6.90 1.50 2.48 1.20 1.20 104.00 3.00 4.00 <1.0 55.00 <2.0 120.00 12.50 2.25 01.07.2019 406.79 7.73 7.19 0.84 2.48 1.10 1.10 87.20 3.00 3.00 1.00 39.00 <2.0 91.00 9.41 2.06 05.08.2019 172.37 7.13 6.85 0.74 1.53 0.64 0.62 80.20 2.00 2.00 <1.0 27.00 <2.0 73.00 <1.0 1.86 <0.002 0.04 0.04 <0.003 0.42 0.30 0.36 0.12 <1.0 02.09.2019 149.53 7.70 8.29 0.60 0.86 0.80 0.70 90.40 <1.0 3.00 2.00 25.00 <2.0 60.00 7.20 1.90 07.10.2019 94.79 7.48 7.15 0.65 1.91 0.97 0.95 94.20 2.00 2.00 <1.0 30.00 4.00 75.00 <1.0 2.23 <0.002 0.03 0.04 <0.003 0.44 0.50 0.52 0.12 <1.0 04.11.2019 52.41 7.59 7.81 0.66 2.81 1.00 0.90 139.00 3.00 3.00 <1.0 63.00 16.00 150.00 15.90 2.44 09.12.2019 58.38 7.49 7.20 <0.3 0.76 1.10 1.10 150.00 <1.0 3.00 <1.0 62.00 11.00 120.00 14.80 2.49 Lower avg. 153.19 7.53 7.75 1.22 2.06 1.14 1.09 152.00 2.17 3.92 0.92 79.75 7.00 149.92 15.23 2.41 0.00 0.05 0.06 0.00 0.55 0.61 0.47 0.18 0.00 Upper avg.. 153.19 7.53 7.75 1.24 2.06 1.14 1.09 152.00 2.42 3.92 1.42 79.75 7.83 149.92 15.48 2.41 0.00 0.05 0.06 0.00 0.55 0.61 0.47 0.18 1.00 Minimum 47.70 7.13 6.85 0.30 0.76 0.64 0.62 80.20 1.00 2.00 1.00 25.00 2.00 60.00 1.00 1.86 0.00 0.03 0.04 0.00 0.42 0.30 0.36 0.12 1.00 Maximum 424.73 7.73 9.19 4.10 4.91 1.70 1.60 465.00 6.00 7.00 2.00 240.00 42.00 290.00 76.50 3.00 0.00 0.06 0.09 0.00 0.69 0.93 0.53 0.27 1.00 More than 70% >LOD yes yes yes yes yes yes yes yes yes yes no yes no yes yes yes no yes yes no yes yes yes yes no n 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 St.dev 129.41 0.15 0.75 1.17 1.24 0.31 0.29 105.94 1.51 1.51 0.51 60.74 11.64 75.88 20.41 0.37 0.00 0.02 0.03 0.00 0.14 0.27 0.08 0.08 0.00

105

Tanaelva Date Qs pH KOND TURB860 SPM TOC DOC Part. C PO4-P TOTP TDP NO3-N NH4-N TOTN Tot. Part. N SiO2 Ag As Pb Cd Cu Zn Ni Cr Hg DD.MM.YYYY [m3/s] [] [mS/m] [FNU] [mg/l] [mgC/l] [mgC/l] [µgC/l] [µgP/l] [µgP/l] [µgP/l] [µgN/l] [µgN/l] [µgN/l] [µgN/l] [mgSiO2/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [ng/l] 07.01.2019 84.62 7.26 7.03 0.48 0.45 1.90 1.80 107.00 2.00 6.00 3.00 94.00 11.00 170.00 12.70 9.79 04.02.2019 66.31 7.41 7.35 0.57 1.63 1.80 1.70 111.00 5.00 6.00 6.00 69.00 <2.0 130.00 1.00 10.54 <0.002 0.10 0.03 0.01 0.35 0.43 0.27 0.28 <1.0 04.03.2019 73.21 7.20 7.45 0.39 <0.33 1.50 1.50 106.00 5.00 5.00 7.00 35.00 <2.0 170.00 <1.0 10.05 01.04.2019 66.28 7.19 8.20 0.57 1.67 1.40 1.50 368.00 10.00 17.00 10.00 90.00 <2.0 220.00 31.20 10.35 06.05.2019 317.72 7.00 3.69 2.20 3.82 4.60 4.30 373.00 2.00 11.00 3.00 34.00 4.00 140.00 40.40 5.68 <0.002 0.05 0.07 0.00 0.99 8.90 1.54 0.66 <1.0 03.06.2019 925.93 7.09 2.78 0.80 2.23 4.20 4.20 294.00 2.00 11.00 4.00 6.00 <2.0 110.00 <1.0 4.52 01.07.2019 446.30 7.30 4.04 <0.3 1.52 3.50 3.50 232.00 3.00 6.00 3.00 6.00 11.00 100.00 12.40 5.12 05.08.2019 135.86 7.43 6.15 <0.3 0.41 1.70 1.70 66.80 2.00 4.00 1.00 13.00 <2.0 85.00 <1.0 6.02 <0.002 0.07 0.03 0.00 0.53 0.48 0.29 0.22 <1.0 02.09.2019 195.70 7.34 5.48 0.40 1.18 2.60 2.60 273.00 2.00 8.00 2.00 2.00 5.00 91.00 40.30 5.70 30.09.2019 180.25 7.27 5.07 <0.3 0.57 2.70 2.60 172.00 2.00 4.00 1.00 17.00 6.00 110.00 15.60 6.15 <0.002 0.04 0.01 <0.003 0.38 1.00 0.31 0.23 <1.0 04.11.2019 86.85 7.36 5.53 0.59 0.74 2.60 2.60 163.00 <1.0 4.00 2.00 34.00 5.00 130.00 20.20 8.81 02.12.2019 66.72 7.36 6.58 0.34 <0.33 2.10 2.00 130.00 <1.0 3.00 1.00 67.00 39.00 200.00 20.80 9.15 Lower avg. 220.48 7.27 5.78 0.53 1.19 2.55 2.50 199.65 2.92 7.08 3.58 38.92 6.75 138.00 16.22 7.66 0.00 0.06 0.04 0.00 0.56 2.70 0.60 0.35 0.00 Upper avg.. 220.48 7.27 5.78 0.60 1.24 2.55 2.50 199.65 3.08 7.08 3.58 38.92 7.58 138.00 16.47 7.66 0.00 0.06 0.04 0.00 0.56 2.70 0.60 0.35 1.00 Minimum 66.28 7.00 2.78 0.30 0.33 1.40 1.50 66.80 1.00 3.00 1.00 2.00 2.00 85.00 1.00 4.52 0.00 0.04 0.01 0.00 0.35 0.43 0.27 0.22 1.00 Maximum 925.93 7.43 8.20 2.20 3.82 4.60 4.30 373.00 10.00 17.00 10.00 94.00 39.00 220.00 40.40 10.54 0.00 0.10 0.07 0.01 0.99 8.90 1.54 0.66 1.00 More than 70% >LOD yes yes yes yes yes yes yes yes yes yes yes yes no yes yes yes no yes yes yes yes yes yes yes no n 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 St.dev 251.67 0.13 1.67 0.53 1.03 1.05 1.01 105.98 2.54 4.08 2.78 33.14 10.42 43.46 14.71 2.30 0.00 0.02 0.03 0.00 0.30 4.14 0.63 0.21 0.00

106

Pasvikelva Date Qs pH KOND TURB860 SPM TOC DOC Part. C PO4-P TOTP TDP NO3-N NH4-N TOTN Tot. Part. N SiO2 Ag As Pb Cd Cu Zn Ni Cr Hg DD.MM.YYYY [m3/s] [] [mS/m] [FNU] [mg/l] [mgC/l] [mgC/l] [µgC/l] [µgP/l] [µgP/l] [µgP/l] [µgN/l] [µgN/l] [µgN/l] [µgN/l] [mgSiO2/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [µg/l] [ng/l] 07.01.2019 68.73 7.17 3.45 <0.3 0.47 3.00 3.00 169.00 <1.0 4.00 2.00 42.00 73.00 180.00 18.00 5.25 10.02.2019 59.86 6.98 3.23 0.37 <0.38 2.80 2.80 63.70 1.00 12.00 2.00 170.00 <2.0 270.00 <1.0 5.64 0.00 0.07 0.02 0.00 0.94 1.00 1.13 0.13 <1.0 04.03.2019 101.32 7.05 3.44 0.45 1.16 2.70 2.70 400.00 1.00 3.00 2.00 64.00 29.00 260.00 27.30 5.70 01.04.2019 72.47 7.09 3.41 <0.3 0.48 2.90 2.90 180.00 1.00 4.00 2.00 57.00 <2.0 150.00 15.70 5.91 05.05.2019 535.56 6.94 2.90 2.70 15.70 2.70 2.40 1396.00 5.00 28.00 5.00 25.00 82.00 170.00 106.00 4.52 0.01 0.28 0.18 0.03 12.00 4.90 16.40 0.36 <1.0 02.06.2019 420.78 7.14 4.65 1.80 4.77 3.90 3.70 650.00 3.00 12.00 5.00 23.00 <2.0 130.00 <1.0 4.95 01.07.2019 310.38 7.21 3.18 0.31 1.02 3.90 3.90 224.00 4.00 6.00 2.00 <2.0 12.00 110.00 <1.0 4.26 05.08.2019 122.83 7.20 3.37 0.50 0.92 2.90 2.90 205.00 2.00 4.00 1.00 <2.0 4.00 83.00 <1.0 4.44 <0.002 0.07 0.02 0.00 1.21 0.74 5.10 0.16 <1.0 03.09.2019 109.05 7.19 3.36 0.56 0.91 3.20 3.20 212.00 2.00 4.00 2.00 <2.0 4.00 96.00 <1.0 3.86 07.10.2019 120.63 7.18 4.07 0.51 0.85 3.20 3.20 193.00 1.00 6.00 2.00 6.00 6.00 110.00 23.00 4.31 0.00 0.14 0.03 0.01 1.71 1.80 14.70 0.16 3.00 04.11.2019 73.93 7.14 3.44 0.39 <0.41 3.20 3.00 259.00 2.00 6.00 <1.0 22.00 4.00 130.00 37.50 5.16 01.12.2019 56.64 7.22 3.44 <0.3 <0.38 3.20 3.10 146.00 <1.0 3.00 <1.0 32.00 12.00 130.00 16.40 5.51 Lower avg. 171.02 7.13 3.49 0.63 2.19 3.13 3.07 341.47 1.83 7.67 2.08 36.75 18.83 151.58 20.32 4.96 0.01 0.14 0.07 0.01 3.96 2.11 9.33 0.20 0.75 Upper avg.. 171.02 7.13 3.49 0.71 2.29 3.13 3.07 341.47 2.00 7.67 2.25 37.25 19.33 151.58 20.74 4.96 0.01 0.14 0.07 0.01 3.96 2.11 9.33 0.20 1.50 Minimum 56.64 6.94 2.90 0.30 0.38 2.70 2.40 63.70 1.00 3.00 1.00 2.00 2.00 83.00 1.00 3.86 0.00 0.07 0.02 0.00 0.94 0.74 1.13 0.13 1.00 Maximum 535.56 7.22 4.65 2.70 15.70 3.90 3.90 1396.00 5.00 28.00 5.00 170.00 82.00 270.00 106.00 5.91 0.01 0.28 0.18 0.03 12.00 4.90 16.40 0.36 3.00 More than 70% >LOD yes yes yes yes yes yes yes yes yes yes yes yes yes yes no yes yes yes yes yes yes yes yes yes no n 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 12.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 4.00 St.dev 160.48 0.09 0.45 0.75 4.39 0.41 0.41 364.30 1.35 7.11 1.36 46.78 28.27 59.98 29.54 0.67 0.01 0.10 0.08 0.01 5.37 1.91 7.39 0.11 1.00

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6.2 Riverine loads in 2019

River Estimate Flow rate SPM TOC PO4-P TOTP NO3-N NH4-N TOTN SiO2 Ag As Pb Cd Cu Zn Ni Cr Hg

[kg]

tonnes]

[tonnes] [tonnes] [tonnes] [tonnes] [tonnes] [tonnes] [tonnes] [tonnes] [tonnes] [tonnes] [ [tonnes] [tonnes] [tonnes] [tonnes] [tonnes] 1000 m3/d 1000

Glomma ved Sarpsfoss avg. 68724.28 231900.82 120633.42 219.28 505.23 10707.40 223.21 16007.19 93974.33 0.17 4.00 5.92 0.34 42.72 150.61 19.44 6.21 51.56 Alna avg. 157.44 4841.36 557.96 6.83 8.33 46.57 0.90 77.03 349.32 0.00 0.02 0.04 0.00 0.20 0.62 0.06 0.02 0.08 Drammenselva avg. 30040.03 16797.21 39925.22 21.98 58.82 3554.80 104.50 5080.43 32190.31 0.01 1.43 0.72 0.09 6.95 19.58 4.74 1.11 31.96 Numedalslågen avg. 10191.47 16540.46 16477.72 21.40 39.52 1143.44 132.34 1814.00 13477.48 0.03 0.70 1.41 0.07 3.42 18.94 1.42 0.72 8.73 Skienselva avg. 23520.99 6129.18 22347.84 11.52 31.22 1166.48 89.05 1948.08 18786.42 0.00 0.75 0.36 0.07 4.12 15.67 2.04 0.57 14.61 Otra avg. 12232.63 4153.88 15583.92 6.08 14.78 331.33 62.22 891.05 7410.25 0.02 0.66 1.12 0.12 2.47 12.32 2.15 0.40 5.37 Orreelva avg. 470.05 2741.08 1582.42 4.42 14.64 134.27 4.54 294.36 435.79 0.00 0.06 0.07 0.00 0.28 0.59 0.22 0.04 0.47 Vosso (Bolstadelvi) avg. 6827.16 1977.47 3476.21 2.51 9.45 224.32 5.57 407.18 2569.10 0.01 0.21 0.17 0.01 0.90 1.95 0.71 0.12 3.85 Orkla avg. 5917.46 2116.51 6892.11 2.35 8.67 456.02 6.21 723.95 6328.97 0.00 0.25 0.04 0.09 12.01 25.35 1.66 0.47 2.46 Vefsna avg. 13823.15 4429.22 7107.55 5.88 17.86 180.85 7.52 446.39 6947.07 0.00 0.50 0.12 0.01 1.47 1.60 1.37 0.32 3.92 Altaelva avg. 7844.14 12836.29 9656.52 5.68 16.59 81.31 2.84 358.55 12569.73 0.01 0.29 0.03 0.00 1.21 0.38 0.64 0.49 1.54 Bjerkreimselva avg. 3507.88 634.09 1977.17 1.50 5.20 429.91 6.10 566.94 1935.28 0.00 0.13 0.26 0.03 2.18 9.07 0.80 0.15 0.00 Vikedalselva avg. 832.40 384.71 455.99 0.53 1.48 59.68 2.63 88.80 291.35 0.00 0.08 0.02 0.00 0.11 0.73 0.09 0.01 0.00 Nausta avg. 1863.29 919.65 1815.40 3.01 5.87 68.83 5.09 143.98 911.89 0.00 0.03 0.06 0.00 0.17 0.70 0.08 0.05 0.30 Driva avg. 7282.59 2369.66 3377.20 3.32 7.06 415.01 3.50 600.69 8227.34 0.00 0.09 0.03 0.00 1.70 2.83 0.46 0.34 4.54 Nidelva (Tr.heim) avg. 7588.16 5712.60 7642.28 7.22 12.21 282.18 10.17 525.36 5692.27 0.00 0.27 0.13 0.00 1.99 2.30 2.11 0.84 0.00 Målselva v/gml E6-brua avg. 15100.26 12136.52 6222.93 14.36 20.53 358.34 20.45 702.83 12621.10 0.00 0.26 0.32 0.00 2.94 3.04 2.59 0.99 0.00 Tanaelva avg. 20199.89 13053.15 24965.88 18.10 60.83 147.89 41.59 881.89 43729.35 0.00 0.40 0.32 0.03 4.92 31.97 6.21 3.10 0.00 Pasvikelva avg. 17903.23 36221.82 21209.53 20.08 82.30 158.25 187.92 931.40 30814.40 0.07 1.39 0.82 0.14 52.28 23.16 89.30 1.85 4.99 Vegårdselva avg. 1572.04 912.26 3492.02 1.30 3.92 81.32 12.30 192.52 1560.15 0.00 0.16 0.24 0.02 0.41 3.72 0.30 0.12 0.78

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